M.T. Taner, T. Berge, J.D. Walls, M. Smith, G. Taylor, D. Dumas, M.B. Carr | Journal of Petroleum | October 2001

We present a new method for calibrating a classified 30 seismic volume. The classification process employs a Kohonen self-organizing map, a type of unsupervised artificial neural network; the subsequent calibration is performed using one or more suites of well logs. Kohonen self-organizing maps and other unsupervised clustering methods generate classes of data based on the identification of various discriminating features. These methods seek an organization in a dataset and form relational organized clusters. However, these clusters may or may not have any physical analogues in the real world. In order to relate them to the real world, we must develop a calibration method that not only defines the relationship between the clusters and real physical properties, but also provides an estimate of the validity of these relationships. With the development of this relationship, the whole dataset can then be calibrated.

The clustering step reduces the multi-dimensional data into logically smaller groups. Each original data point defined by multiple attributes is reduced to a one- or two-dimensional relational group. This establishes some logical clustering and reduces the complexity of the classification problem. Furthermore, calibration should be more successful since it will have to consider less variability in the data.

In this paper, we present a simple calibration method that employs Bayesian logic to provide the relationship between cluster centres and the real world. The output will give the most probable calibration between each self-organized map node and wellbore measured parameters such as lithology, porosity and fluid saturation. The second part of the output comprises the calibration probability.

The method is described in detail, and a case study is briefly presented using data acquired in the Orange River Basin, South Africa. The method shows promise as an alternative to current techniques for integrating seismic and log data during reservoir characterization.


Recent changes in oil company objectives from exploration to production have been accompanied by the increased use of seismic methods for lithology prediction and reservoir characterization. A number of seismic attributes have been developed specifically for these purposes. Early successes came from using velocities and various bright spot technologies as direct hydrocarbon indicators. Subsequently, AVO (Amplitude Variation with Offset) analysis provided additional information on the lithologies and fluid contrasts across subsurface boundaries.

However, although AVO has been used successfully in many parts of the World and has evolved as a valid exploration tool, it does not provide an unambiguous definition of reservoir potential. Other seismic attributes introduced in the mid- 1970s can provide additional perspectives on subsurface geology, lithology and reservoir characteristics. For example, geometrical attributes provide information on geological and stratigraphic settings; complex trace attributes can be used to identify sequence boundaries; relative absorption can be used to indicate the presence of gas; while acoustic impedance is often an effective tool for porosity discrimination. The combined use of a number of these attributes can minimize uncertainties in reservoir assessments.

Seismic attributes relate to reservoir properties in a non-linear manner. Rock physics gives us a view of these relationships under certain assumed sets of conditions. Due to the non-linear nature of the problem, artificial neural networks are applicable and are now moving from experimental to mainstream use in the geosciences (readers are referred to Haykin (1 994) and Bishop (1999) for detailed descriptions of the technologies involved). A primary attraction of neural networks is that they can be trained to give correct predictions in complicated and non-linear cases. In one class of artificial neural network, the training is “supervised”; an example would be where input seismic data and output rock properties are provided from a training data set. In a second type of network, the training is “unsupervised”. In this latter case, the network searches for some structure or pattern in the input data set, and forms organized clusters or classes as outputs.

In this paper, we discuss a type of unsupervised neural network developed by the Finnish scientist Teuvo Kohonen, and known as a Kohonen self-organizing map (SOMj (Kohonen, 2001). We describe how an SOM can be applied to lithology prediction from seismic data. We present a brief case study from the recently discovered Ibhubesi field, Orange River Basin, western South Africa.

Lithology prediction or reservoir characterization with seismic attributes is an example of a problem that involves the reduction of many observations into a smaller number of recognizable classes. In an early example of the adaptation of the Kohonen self-organizing map technology for hydrocarbon exploration, Morice et al. (1 996) were able to discriminate lithofacies using seismic trace shape. This method provided information in the form of 2D maps or surfaces. In our method, we treat each seismic data sample as an N-dimensional vector; that is, each data sample is defined by a combination of physical and geometric seismic attributes (Carr et al., 200 1). These include amplitude, phase, instantaneous frequency, lateral continuity, and bedding geometry. This definition provides several advantages: (i) clustering and classification will be 3D rather than 2D; and (ii) clustering will be based on the combination of attributes used. Thus, if mainly geometrical attributes are used, the clustering will be based on sub-surface structural and stratigraphic morphology. If physical attributes are used, the clustering will be based predominantly on lithological differentiation. While individual attribute volumes can be a valuable interpretation aid (e.g. coherency or acoustic impedance j, their combination via Kohonen self-organizing map clustering can provide significantly greater discrimination of geology and rock properties.

Schematic diagram of a single neural node (neuron)
Fig. 1. Schematic diagram of a single neural node (neuron), showing input connections and weightings. Kohonen self-organizing maps consist of a single layer of neural nodes arranged in 1- or 21) arrays (see Figs 2 and 3 respectively).


Kohonen’s self-organizing maps are simple analogues of the human brain’s ability to organize information in a logical manner. The cerebral cortex of the human brain contains billions of neurons with many billions of connections (synapses) between them. It is divided into functional divisions in an orderly manner; the most important divisions are the motor cortex, sensory cortex, visual cortex and auditory cortex. The auditory cortex, for example, is subdivided into many smaller units, each functioning as cognitive parts for auditory signal recognition. It is believed that some of these are trained in a supervised manner, while others are developed in an unsupervised, self-organizing manner. Topologically adjacent cortical areas perform somewhat related cognitive functions. Kohonen’s method simulates the brain’s unsupervised learning process in an elegant and simple manner.

Kohonen’s self-organizing maps consist of a single layer of neurons organized in one-, two- and multi-dimensional arrays. Each neuron has as many input connections as there are attributes to be used in the classification, as shown in Fig. 1. The training procedure consists of finding the neuron with weights that are closest to the input data vector, and declaring that neuron as the “winning” neuron. There are several methods for determining the degree of similarity. In the simplest method, each input is weighted by a neuron’s corresponding weight vector and the results are summed. This represents the net input of the particular neuron. Let k represent the kth neuron where N attributes are used. Input data samples are represented by x and individual neural weights by w. Then the net input will be (in terms of a vector scalar product):
net input of a particular neuron
The vector scalar product (1) will give the projection of one vector onto the other. The neuron with the largest net input is selected as the winner. Then the weights of all neurons in the vicinity of the winning neuron are adjusted by an amount that is inversely proportional to the distance. The radius of the accepted vicinity is reduced as the number of iterations increases. The training process is terminated if RMS errors of all inputs are reduced to an acceptable level, or if a prescribed number of iterations is reached.

A ID Kohonen neural network
Fig. 2. A ID Kohonen neural network. Input is a 3D volume of seismic attribute data, in this case comprised of three attributes. Data in this volume can be separated or clustered into “clouds” of varying size and shape. Each neuron represents the cluster centre of these clouds and each cluster centre is a Kohonen class. Note that neurons are shown equally spaced in a straight line only for illustration. The network can be thought of as a string that can have any shape with any distance between neurons, depending on the input data.

Kohonen’s method consists of one layer of neurons and uses a method of competitive learning known as “winner takes all”. Since the “winner takes all” logic may result in one neuron dominating the training, some modification is necessary. The modification scheme examines the winning rate of each neuron, and if it identifies a single dominant neuron, it then activates a “conscience” algorithm that allows other neurons to participate in the learning procedure. (Haykin, 1994)


Neurons can be organized in any topological manner. Fig. 2 shows a one-dimensional organization. This will allow clustering with a one-dimensional topological relationship, that is, each adjacent neuron will have smaller differences than those located further away. The differences will gradually increase with increasing topological distance. Each neuron is connected to the input data with its own weights. In the case of the Kohonen self-organizing map, these weights will be equivalent to the actual attributes representing the mean attribute values of each cluster. This feature of the SOM is quite different from the feed-forward fully-connected class of artificial neural networks, where the weights do not relate directly to the input data.

The same characteristics will prevail in two-dimensional mapping as shown in Fig. 3. In this case, each neuron is directly connected to four neighbouring neurons. Their differences will be proportional to the topological distance between the neurons. We can think of three-dimensional mapping in the same way, although, in practice, one or two-dimensional mapping is usually employed.


In the method presented here, we used Bayesian logic to establish the relationship between lithological classes which have been identified in the borehole and the nodes on the Kohonen self-organizing map. Bayesian decision-making states that if we know the probability density of different classes of data, we can minimize the classification error by

A 2D Kohonen neural network
Fig. 3. A 2D Kohonen neural network. Input is a 3D volume of seismic attribute data, in this case comprised of three attributes. Data in this volume can be separated or clustered into “clouds” of varying size and shape. Each neuron represents the cluster center of these clouds and each cluster center is a Kohonen class. Note that neurons are shown arranged in a flat, regularly spaced grid only for illustration. The network can be thought of as a flexible fabric that can have any shape depending on the input data.

Bayesian boundaries for three different probability densities
Fig. 4. Bayesian boundaries for three different probability densities. The boundaries separate data in one cloud from data in another.

assigning a new (unknown) sample to the class with the highest probability (Duda and Hart, 1973). For example, in a one-dimensional case of three classes with their associated probability density functions (as shown in Fig. 4), we can establish Bayesian boundaries at the points of equal probabilities between each pair of classes. Therefore, any unknown data can be assigned to the class with highest probability density.

Plot of input data
Fig. 5. Plot of input data as a vector in 3D space (blue); neural weight vector plots as the red line. The green line is the Euclidean distance between the two vectors.

Computation of probability by Euclidean distance and Gaussian function.
Fig. 6. Computation of probability by Euclidean distance and Gaussian function.

To calibrate the clusters on the Kohonen self-organizing map, we use a set of seismic training data which consists of seismic attribute values together with a set of user-assigned lithological classes, usually derived from well logs. To accomplish Bayesian classification, we compute the probability density function of each class in the SOM topology. The basis of the probability density computation is the Euclidean distance of each calibration dataset to the individual neurons in the Kohonen SOM. We assume that the closer the input data is to a particular neuron, the higher the probability will be that the neuron represents the class of the training input data. We also assume that probability density has a Gaussian distribution. Its shape will be controlled by the variance of the error between the Kohonen self-organizing map and the training data set. A smaller shaping factor will produce a sharper, more quickly-decaying Gaussian function with respect to Euclidean distance. Larger shaping factors will produce a wider, more slowly-varying function.

The first pass made through the data attempts to determine the variance of the error. This provides a basis for the Gaussian shaping factor. The Euclidean distances between data points and each neural node are computed. Let x(i,n) be the nth attribute of the ith individual training data vector, and w(k,n) the nth neural weight of the (winning) kth neuron. The Euclidean distance (Fig. 5) is computed by:
The Euclidean distance formula
The node with the shortest distance to the input data is declared the winning neuron. This is repeated for all of the training data points and an RMS error of Euclidean distance is computed:
RMS error of Euclidean distance calculation
where In is the number of training data samples. We compute the Gaussian shaping factor such that, if the input Euclidean distance is equal to RMS error distance, the probability is equal to some constant, say 0.5. This corresponds to setting the probability to 50% if the distance is at RMS error distance. The Gaussian probability function (Fig. 6) is given by:
The Gaussian probability function
We determine the scale factor by setting distance equal to the RMS error which will result in = -ln.2. If we wish to make the shaping more sharp, we can decrease the value of exp. to 0.25, and so on.

In the second pass, we compute the probability density function at each neural location for each class separately. We then compare the probability density functions and determine the class with the highest probability at each neuron. We use Equation 2 to compute the Euclidean distance between the input data and each of the neurons in the Kohonen self-organizing map. These distances are input to the Gaussian function (Equation 4) to compute the probability of classification at each neuron. These probabilities are accumulated at each neuron for each class separately. The resulting accumulated values are divided by the number of data samples belonging to that particular class.

Upon completing the computation for all classes, we produce two tables; one for the lithology classes with the highest probability for each Kohonen SOM neural node; and a companion table of corresponding probability densities. This calibration procedure can be conducted on multi-well data and is applicable for vertical, deviated and horizontal wells. Furthermore, the procedure does not require the computation of synthetic seismograms. The method is rapid and easily automated.


The case study focuses on the Orange River Basin, western South Africa (Fig. 7). The Orange River delta is less well known than the Niger and Congo deltas and its deepwater portion has yet to be drilled, but recent oil and gas discoveries have confirmed its exploration potential. On the basis of these discoveries, at least three hydrocarbon systems are thought to exist in the area, comprising: an oil system in the early rift succession (e.g. at well A-J1); (ii) a gas system in the late rift succession (e.g. Kudu field: Fig. 7); and (iii) a gas system in the Albian-Aptian drift succession (e.g. Ibhubesi field: Fig. 7). This case history makes use of calibrated seismic attributes for delineation of the reservoir at Ibhubesi.

In 2000, Forest Oil International shot a 312 sq. km 3D seismic survey in Block 2A around well A-K1, which had tested gas but had been abandoned in 1986. The well was thought to have tested a small non-commercial structural trap, but the 3D survey showed the trap was stratigraphic and of regional extent; it was subsequently designated the Ibhubesi field. The 3D survey area may in fact only cover a small portion in the south of the field, which could eventually produce as much as 50 TCF of gas. Attribute processing and other inversion techniques were used to predict the presence and properties of the reservoir, to assess the potential field reserves, and to plan a drilling campaign to delineate the field.

Map of South Africa offshore license blocks with study area
Fig. 7. Map of South Africa offshore license blocks with study area (Ibhubesi gasfield) indicated by red oval (courtesy of Forest Oil).

Individual gas accumulations in meandering fluvial channel deposits and other component facies of a fluvial-deltaic system were clearly identified in the resulting volumes.

A four-well drilling programme was undertaken to evaluate the field and prove-up a core development area with sufficient reserves to be developed. Each well tested individual compartments with 28 to 520 MCF gas per well. Well A-K2 tested 30 MCF gas and over 600 brls of condensate per day from a 20m thick pay sand on a 3/4” choke with a flowing tubing pressure of 2,200 psi. The reservoir characteristics were better than expected and comprised clean, well-sorted sandstone with an average porosity of 2 1 % (up to 25%) and almost no water saturation (no water was produced during testing). No significant reservoir pressure drawdown occurred during a 12 hr test.

In the second well, A-V1, a 15m gas-bearing sandbody of similar quality to that in A-K2 was found, but the drillstring twisted-off before drilling a second, deeper sand that was subsequently penetrated successfully in a sidetrack. This well confirmed the presence of additional reserves. Interestingly, the lower gas sand at A-V1 was deeper than the lowest proven gas and highest proven water at A-K1, clearly showing that this is a separate reservoir and stratigraphic trap. As the well results came in, the initial prediction of a regional-scale stratigraphic trap was gradually proved.

A third well was targeted to test the largest and brightest anomaly in the dataset, one which appeared to have the higher reserves than the other anomalies. The well penetrated two thick and porous sand units as predicted, but the reservoirs only contained water with a low gas saturation.

The fourth well, A-Y1, is currently being tested. This well encountered 60m of net pay in three zones. The lowest zone, a 2m thick sand, tested at a rate of 18 MM cf/d gas and 90 brl/d of condensate. The well flowed at 7 1 MM cf/d gas and 1,340 b/d condensate from combined tests of the upper two zones. This is the highest gas test rate so far achieved in South Africa. As a result of the drilling campaign, total reserves within the 3D area were estimated at about 1.15 TCF of gas.

A 100 class (10x10) Kohonen self-organizing map calibrated to one of four lithologies.
Fig. 8. A 100 class (10×10) Kohonen self-organizing map calibrated to one of four lithologies. Each lithology is identified by a specific colour for ease of recognition. The value inside each Kohonen class represents the relative probability that the class corresponds to the indicated lithology with higher values representing greater confidence. Class numbers (1-100) are shown for classes around the outer edge of the 10 x 10 grid.

Amplitude time-slice at reservoir level showing the locations of three producing wells.
Fig. 9. Amplitude time-slice at reservoir level showing the locations of three producing wells. This amplitude map does not clearly respond to gas for all well locations.

Seismic sub-volume from final calibrated Kohonen classification showing the “gas sand” bodies (green) in relation to three producing wells.
Fig. 10. Seismic sub-volume from final calibrated Kohonen classification showing the “gas sand” bodies (green) in relation to three producing wells.

Well logs from wells AK-1 and AK-2 were used to classify four different lithologies: siltstone, shale, wet sand and gas sand. The classification was based on a combination of well log measurements including density, gamma, neutron, sonic and resistivity. Shale, siltstone and sandstone were defined by their gamma and neutron-density responses; gas sand versus wet sand were determined from the resistivity log. Well log depth values were converted to equivalent seismic time values using synthetic seismograms. In general, there were good ties over the reservoir zone.

A suite of post-stack seismic attributes was computed from the 3D seismic amplitude volume and were used as input to a Kohonen self-organizing map. The attributes used were: trace envelope, first derivative of envelope, second derivative of envelope, instantaneous phase, instantaneous frequency, bandwidth, wavelet envelope, relative acoustic impedance, dip of maximum similarity, and similarity (see Taner, 2000). These attributes represent a range of important physical and geometrical attributes and were chosen primarily from experience with other similar projects.

A 10 x 10 (1 00-class) rectangular self-organizing map topology was selected. The size of the network was chosen as the smallest that would provide a sufficient number of classes for the discrimination of subtle differences in lithology. Experience has shown that a 10 x 10 topology is generally adequate.

A relative probability value for each Kohonen class was computed. The probability function was generated using the root-mean-square (RMS) clustering distance as the 50% probability value. This provided a starting value for the Gaussian shaping factor. The maximum probability for each cluster centre was determined by comparison. The final calibration and related probabilities are shown in Fig. 8. From this calibration, each sample in the 3D survey, within a selected 2-way time interval, was identified as belonging to one of the four lithology classes.

A time slice at the reservoir level from the 3D seismic amplitude volume is depicted in Fig. 9. The three wells shown are producers. The brightest amplitudes correspond mainly to the location of well AK-2. Well AK-1 is located near the edge of a high amplitude anomaly; Well AV-1 does not correspond to an amplitude anomaly. In Fig. 10, we show a seismic amplitude volume with part of the inside of the volume cut away to show the location of the 3D gas sandbodies from Bayesian calibration (in green). Each of the three producing wells appear to intersect the computed sandbodies. The sandbody class distribution is consistent with a meandering fluvial channel geometry in a fluvial-deltaic system. This analysis will be compared against future drilling results, but so far the predictions of gas sand location have been born out by drilling results.


In this project we used an unsupervised clustering method known as a Kohonen self-organizing map to separate a suite of seismic attributes into 100 individual classes. We then used a Bayesian logic method to redefine these classes in terms of lithology descriptions based on data from well logs. The method was tested on a dataset from the Orange River Basin, offshore South Africa. We have concluded from this work that:

  • Kohonen self-organizing maps can be used to arrange multiple seismic attributes into a logical set of classes.
  • These classes can be calibrated to non-seismic data such as well logs using a Bayesian logic method.
  • The method was tested on a real seismic and well log dataset and was shown to provide results which were consistent with the accepted geologic and stratigraphic setting.
  • We were able to discriminate at least four lithologies – siltstone, shale, wet sand and gas sand.


We would like to thank Forest Oil Corporation, Anschutz Corporation, SOEKOR and Petroleum Agency SA for the contribution of the seismic and log data used in this work. Comments by P. M. Wong (University of New South Wales) and M. Nikravesh (University of California Berkeley) on a previous version of this paper are acknowledged with thanks.

BISHOP, C. M., 1999. Neural Networks for Pattern Recognition. Oxford University Press, pp. 164-193.

CARR, M., COOPER, R., SMITH, M., TANER, M. and TAYLOR, G., 2001. The integration of surface seismic and log data to generate a rock and fluid properties volume – a South Texas Example. Presented at the 43rd Annual PBGS Exploration Meeting, Midland, Texas.

DUDA, R. 0. and HART, P.E., 1973. Pattern Classification and Scene Analysis. Wiley Interscience.

HAYKIN, S., 1994. Neural Networks, A comprehensive foundation. Macmillan Publishing Co., pp. 236-284.

KOHONEN, T., 2001. Self Organizing Maps. Springer Series in Information Sciences, 30, Springer Verlag, Berlin.

MORICE, M., KESKES, N., and JEANJEAN, F., 1996. Manual and automatic seismic facies analysis on SISMAGE Tm workstation. Annual Meeting Abstracts, Society of Exploration Geophysicists, 320- 323

OJA, F. and KASKI, S., (Eds), 1999. Kohonen Maps. Elsevier Science.

TANER, M. T., 2000. Attributes Revisited. Rock Solid Images report, Sept. 2000 (see www.rocksolidimages.com).

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    • - Communicate who you are to high-level executives in exploration and development
    • - Avoid common social media pitfalls
    • - Optimize your online presence to best garner attention from recruiters
    • - Stay relevant
    • - Create content of interest
    • - Establish yourself as a thought leader in your given area of specialization
    Laura Cuttill
    Practice Lead, Advertas

    As a 20-year marketing veteran marketing in oil and gas and serial entrepreneur, Laura has deep experience in bringing technology products to market and growing sales pipeline. Armed with a marketing degree from Texas A&M, she began her career doing technical writing for Schlumberger and ExxonMobil in 2001. She started Advertas as a co-founder in 2004 and began to leverage her upstream experience in marketing. In 2006, she co-founded the cyber-security software company, 2FA Technology. After growing 2FA from a startup to 75% market share in target industries, and the subsequent sale of the company, she returned to Advertas to continue working toward the success of her clients, such as Geophysical Insights. Today, she guides strategy for large-scale marketing programs, manages project execution, cultivates relationships with industry media, and advocates for data-driven, account-based marketing practices.

    Fabian Rada
    Sr. Geophysicist, Petroleum Oil & Gas Services

    Statistical Calibration of SOM results with Well Log Data (Case Study)

    The first stage of the proposed statistical method has proven to be very useful in testing whether or not there is a relationship between two qualitative variables (nominal or ordinal) or categorical quantitative variables, in the fields of health and social sciences. Its application in the oil industry allows geoscientists not only to test dependence between discrete variables, but to measure their degree of correlation (weak, moderate or strong). This article shows its application to reveal the relationship between a SOM classification volume of a set of nine seismic attributes (whose vertical sampling interval is three meters) and different well data (sedimentary facies, Net Reservoir, and effective porosity grouped by ranges). The data were prepared to construct the contingency tables, where the dependent (response) variable and independent (explanatory) variable were defined, the observed frequencies were obtained, and the frequencies that would be expected if the variables were independent were calculated and then the difference between the two magnitudes was studied using the contrast statistic called Chi-Square. The second stage implies the calibration of the SOM volume extracted along the wellbore path through statistical analysis of the petrophysical properties VCL and PHIE, and SW for each neuron, which allowed to identify the neurons with the best petrophysical values in a carbonate reservoir.

    Heather Bedle
    Assistant Professor, University of Oklahoma

    Heather Bedle received a B.S. (1999) in physics from Wake Forest University, and then worked as a systems engineer in the defense industry. She later received a M.S. (2005) and a Ph. D. (2008) degree from Northwestern University. After graduate school, she joined Chevron and worked as both a development geologist and geophysicist in the Gulf of Mexico before joining Chevron’s Energy Technology Company Unit in Houston, TX. In this position, she worked with the Rock Physics from Seismic team analyzing global assets in Chevron’s portfolio. Dr. Bedle is currently an assistant professor of applied geophysics at the University of Oklahoma’s School of Geosciences. She joined OU in 2018, after instructing at the University of Houston for two years. Dr. Bedle and her student research team at OU primarily work with seismic reflection data, using advanced techniques such as machine learning, attribute analysis, and rock physics to reveal additional structural, stratigraphic and tectonic insights of the subsurface.

    Jie Qi
    Research Geophysicist

    An Integrated Fault Detection Workflow

    Seismic fault detection is one of the top critical procedures in seismic interpretation. Identifying faults are significant for characterizing and finding the potential oil and gas reservoirs. Seismic amplitude data exhibiting good resolution and a high signal-to-noise ratio are key to identifying structural discontinuities using seismic attributes or machine learning techniques, which in turn serve as input for automatic fault extraction. Deep learning Convolutional Neural Networks (CNN) performs well on fault detection without any human-computer interactive work. This study shows an integrated CNN-based fault detection workflow to construct fault images that are sufficiently smooth for subsequent fault automatic extraction. The objectives were to suppress noise or stratigraphic anomalies subparallel to reflector dip, and sharpen fault and other discontinuities that cut reflectors, preconditioning the fault images for subsequent automatic extraction. A 2D continuous wavelet transform-based acquisition footprint suppression method was applied time slice by time slice to suppress wavenumber components to avoid interpreting the acquisition footprint as artifacts by the CNN fault detection method. To further suppress cross-cutting noise as well as sharpen fault edges, a principal component edge-preserving structure-oriented filter is also applied. The conditioned amplitude volume is then fed to a pre-trained CNN model to compute fault probability. Finally, a Laplacian of Gaussian filter is applied to the original CNN fault probability to enhance fault images. The resulting fault probability volume is favorable with respect to traditional human-interpreter generated on vertical slices through the seismic amplitude volume.

    Dr. Jie Qi
    Research Geophysicist

    An integrated machine learning-based fault classification workflow

    We introduce an integrated machine learning-based fault classification workflow that creates fault component classification volumes that greatly reduces the burden on the human interpreter. We first compute a 3D fault probability volume from pre-conditioned seismic amplitude data using a 3D convolutional neural network (CNN). However, the resulting “fault probability” volume delineates other non-fault edges such as angular unconformities, the base of mass transport complexes, and noise such as acquisition footprint. We find that image processing-based fault discontinuity enhancement and skeletonization methods can enhance the fault discontinuities and suppress many of the non-fault discontinuities. Although each fault is characterized by its dip and azimuth, these two properties are discontinuous at azimuths of φ=±180° and for near vertical faults for azimuths φ and φ+180° requiring them to be parameterized as four continuous geodetic fault components. These four fault components as well as the fault probability can then be fed into a self-organizing map (SOM) to generate fault component classification. We find that the final classification result can segment fault sets trending in interpreter-defined orientations and minimize the impact of stratigraphy and noise by selecting different neurons from the SOM 2D neuron color map.

    Ivan Marroquin
    Senior Research Geophysicist

    Connecting Multi-attribute Classification to Reservoir Properties

    Interpreters rely on seismic pattern changes to identify and map geologic features of importance. The ability to recognize such features depends on the seismic resolution and characteristics of seismic waveforms. With the advancement of machine learning algorithms, new methods for interpreting seismic data are being developed. Among these algorithms, self-organizing maps (SOM) provides a different approach to extract geological information from a set of seismic attributes.

    SOM approximates the input patterns by a finite set of processing neurons arranged in a regular 2D grid of map nodes. Such that, it classifies multi-attribute seismic samples into natural clusters following an unsupervised approach. Since machine learning is unbiased, so the classifications can contain both geological information and coherent noise. Thus, seismic interpretation evolves into broader geologic perspectives. Additionally, SOM partitions multi-attribute samples without a priori information to guide the process (e.g., well data).

    The SOM output is a new seismic attribute volume, in which geologic information is captured from the classification into winning neurons. Implicit and useful geological information are uncovered through an interactive visual inspection of winning neuron classifications. By doing so, interpreters build a classification model that aids them to gain insight into complex relationships between attribute patterns and geological features.

    Despite all these benefits, there are interpretation challenges regarding whether there is an association between winning neurons and geological features. To address these issues, a bivariate statistical approach is proposed. To evaluate this analysis, three cases scenarios are presented. In each case, the association between winning neurons and net reservoir (determined from petrophysical or well log properties) at well locations is analyzed. The results show that the statistical analysis not only aid in the identification of classification patterns; but more importantly, reservoir/not reservoir classification by classical petrophysical analysis strongly correlates with selected SOM winning neurons. Confidence in interpreted classification features is gained at the borehole and interpretation is readily extended as geobodies away from the well.

    Heather Bedle
    Assistant Professor, University of Oklahoma

    Gas Hydrates, Reefs, Channel Architecture, and Fizz Gas: SOM Applications in a Variety of Geologic Settings

    Students at the University of Oklahoma have been exploring the uses of SOM techniques for the last year. This presentation will review learnings and results from a few of these research projects. Two projects have investigated the ability of SOMs to aid in identification of pore space materials – both trying to qualitatively identify gas hydrates and under-saturated gas reservoirs. A third study investigated individual attributes and SOMs in recognizing various carbonate facies in a pinnacle reef in the Michigan Basin. The fourth study took a deep dive of various machine learning algorithms, of which SOMs will be discussed, to understand how much machine learning can aid in the identification of deepwater channel architectures.

    Fabian Rada
    Sr. Geophysicist, Petroleum Oil & Gas Servicest

    Fabian Rada joined Petroleum Oil and Gas Services, Inc (POGS) in January 2015 as Business Development Manager and Consultant to PEMEX. In Mexico, he has participated in several integrated oil and gas reservoir studies. He has consulted with PEMEX Activos and the G&G Technology group to apply the Paradise AI workbench and other tools. Since January 2015, he has been working with Geophysical Insights staff to provide and implement the multi-attribute analysis software Paradise in Petróleos Mexicanos (PEMEX), running a successful pilot test in Litoral Tabasco Tsimin Xux Asset. Mr. Rada began his career in the Venezuelan National Foundation for Seismological Research, where he participated in several geophysical projects, including seismic and gravity data for micro zonation surveys. He then joined China National Petroleum Corporation (CNPC) as QC Geophysicist until he became the Chief Geophysicist in the QA/QC Department. Then, he transitioned to a subsidiary of Petróleos de Venezuela (PDVSA), as a member of the QA/QC and Chief of Potential Field Methods section. Mr. Rada has also participated in processing land seismic data and marine seismic/gravity acquisition surveys. Mr. Rada earned a B.S. in Geophysics from the Central University of Venezuela.

    Hal GreenDirector, Marketing & Business Development - Geophysical Insights

    Introduction to Automatic Fault Detection and Applying Machine Learning to Detect Thin Beds

    Rapid advances in Machine Learning (ML) are transforming seismic analysis. Using these new tools, geoscientists can accomplish the following quickly and effectively: a combination of machine learning (ML) and deep learning applications, geoscientists apply Paradise to extract greater insights from seismic and well data for these and other objectives:

    • Run fault detection analysis in a few hours, not weeks
    • Identify thin beds down to a single seismic sample
    • Overlay fault images on stratigraphic analysis

    The brief introduction will orient you with the technology and examples of how machine learning is being applied to automate interpretation while generating new insights in the data.

    Sarah Stanley
    Senior Geoscientist and Lead Trainer

    Sarah Stanley joined Geophysical Insights in October, 2017 as a geoscience consultant, and became a full-time employee July 2018. Prior to Geophysical Insights, Sarah was employed by IHS Markit in various leadership positions from 2011 to her retirement in August 2017, including Director US Operations Training and Certification, the Operational Governance Team, and, prior to February 2013, Director of IHS Kingdom Training. Sarah joined SMT in May, 2002, and was the Director of Training for SMT until IHS Markit’s acquisition in 2011.

    Prior to joining SMT Sarah was employed by GeoQuest, a subdivision of Schlumberger, from 1998 to 2002. Sarah was also Director of the Geoscience Technology Training Center, North Harris College from 1995 to 1998, and served as a voluntary advisor on geoscience training centers to various geological societies. Sarah has over 37 years of industry experience and has worked as a petroleum geoscientist in various domestic and international plays since August of 1981. Her interpretation experience includes tight gas sands, coalbed methane, international exploration, and unconventional resources.

    Sarah holds a Bachelor’s of Science degree with majors in Biology and General Science and minor in Earth Science, a Master’s of Arts in Education and Master’s of Science in Geology from Ball State University, Muncie, Indiana. Sarah is both a Certified Petroleum Geologist, and a Registered Geologist with the State of Texas. Sarah holds teaching credentials in both Indiana and Texas.

    Sarah is a member of the Houston Geological Society and the American Association of Petroleum Geologists, where she currently serves in the AAPG House of Delegates. Sarah is a recipient of the AAPG Special Award, the AAPG House of Delegates Long Service Award, and the HGS President’s award for her work in advancing training for petroleum geoscientists. She has served on the AAPG Continuing Education Committee and was Chairman of the AAPG Technical Training Center Committee. Sarah has also served as Secretary of the HGS, and Served two years as Editor for the AAPG Division of Professional Affairs Correlator.

    Dr. Tom Smith
    President & CEO

    Dr. Tom Smith received a BS and MS degree in Geology from Iowa State University. His graduate research focused on a shallow refraction investigation of the Manson astrobleme. In 1971, he joined Chevron Geophysical as a processing geophysicist but resigned in 1980 to complete his doctoral studies in 3D modeling and migration at the Seismic Acoustics Lab at the University of Houston. Upon graduation with the Ph.D. in Geophysics in 1981, he started a geophysical consulting practice and taught seminars in seismic interpretation, seismic acquisition and seismic processing. Dr. Smith founded Seismic Micro-Technology in 1984 to develop PC software to support training workshops which subsequently led to development of the KINGDOM Software Suite for integrated geoscience interpretation with world-wide success.

    The Society of Exploration Geologists (SEG) recognized Dr. Smith’s work with the SEG Enterprise Award in 2000, and in 2010, the Geophysical Society of Houston (GSH) awarded him an Honorary Membership. Iowa State University (ISU) has recognized Dr. Smith throughout his career with the Distinguished Alumnus Lecturer Award in 1996, the Citation of Merit for National and International Recognition in 2002, and the highest alumni honor in 2015, the Distinguished Alumni Award. The University of Houston College of Natural Sciences and Mathematics recognized Dr. Smith with the 2017 Distinguished Alumni Award.

    In 2009, Dr. Smith founded Geophysical Insights, where he leads a team of geophysicists, geologists and computer scientists in developing advanced technologies for fundamental geophysical problems. The company launched the Paradise® multi-attribute analysis software in 2013, which uses Machine Learning and pattern recognition to extract greater information from seismic data.

    Dr. Smith has been a member of the SEG since 1967 and is a professional member of SEG, GSH, HGS, EAGE, SIPES, AAPG, Sigma XI, SSA and AGU. Dr. Smith served as Chairman of the SEG Foundation from 2010 to 2013. On January 25, 2016, he was recognized by the Houston Geological Society (HGS) as a geophysicist who has made significant contributions to the field of geology. He currently serves on the SEG President-Elect’s Strategy and Planning Committee and the ISU Foundation Campaign Committee for Forever True, For Iowa State.

    Carrie LaudonSenior Geophysical Consultant - Geophysical Insights

    Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells

    This study will demonstrate an automated machine learning approach for fault detection in a 3D seismic volume. The result combines Deep Learning Convolution Neural Networks (CNN) with a conventional data pre-processing step and an image processing-based post processing approach to produce high quality fault attribute volumes of fault probability, fault dip magnitude and fault dip azimuth. These volumes are then combined with instantaneous attributes in an unsupervised machine learning classification, allowing the isolation of both structural and stratigraphic features into a single 3D volume. The workflow is illustrated on a 3D seismic volume from the Denver Julesburg Basin and a statistical analysis is used to calibrate results to well data.

    Ivan Marroquin
    Senior Research Geophysicist

    Iván Dimitri Marroquín is a 20-year veteran of data science research, consistently publishing in peer-reviewed journals and speaking at international conference meetings. Dr. Marroquín received a Ph.D. in geophysics from McGill University, where he conducted and participated in 3D seismic research projects. These projects focused on the development of interpretation techniques based on seismic attributes and seismic trace shape information to identify significant geological features or reservoir physical properties. Examples of his research work are attribute-based modeling to predict coalbed thickness and permeability zones, combining spectral analysis with coherency imagery technique to enhance interpretation of subtle geologic features, and implementing a visual-based data mining technique on clustering to match seismic trace shape variability to changes in reservoir properties.

    Dr. Marroquín has also conducted some ground-breaking research on seismic facies classification and volume visualization. This lead to his development of a visual-based framework that determines the optimal number of seismic facies to best reveal meaningful geologic trends in the seismic data. He proposed seismic facies classification as an alternative to data integration analysis to capture geologic information in the form of seismic facies groups. He has investigated the usefulness of mobile devices to locate, isolate, and understand the spatial relationships of important geologic features in a context-rich 3D environment. In this work, he demonstrated mobile devices are capable of performing seismic volume visualization, facilitating the interpretation of imaged geologic features.  He has definitively shown that mobile devices eventually will allow the visual examination of seismic data anywhere and at any time.

    In 2016, Dr. Marroquín joined Geophysical Insights as a senior researcher, where his efforts have been focused on developing machine learning solutions for the oil and gas industry. For his first project, he developed a novel procedure for lithofacies classification that combines a neural network with automated machine methods. In parallel, he implemented a machine learning pipeline to derive cluster centers from a trained neural network. The next step in the project is to correlate lithofacies classification to the outcome of seismic facies analysis.  Other research interests include the application of diverse machine learning technologies for analyzing and discerning trends and patterns in data related to oil and gas industry.

    Dr. Jie Qi
    Research Geophysicist

    Dr. Jie Qi is a Research Geophysicist at Geophysical Insights, where he works closely with product development and geoscience consultants. His research interests include machine learning-based fault detection, seismic interpretation, pattern recognition, image processing, seismic attribute development and interpretation, and seismic facies analysis. Dr. Qi received a BS (2011) in Geoscience from the China University of Petroleum in Beijing, and an MS (2013) in Geophysics from the University of Houston. He earned a Ph.D. (2017) in Geophysics from the University of Oklahoma, Norman. His industry experience includes work as a Research Assistant (2011-2013) at the University of Houston and the University of Oklahoma (2013-2017). Dr. Qi was with Petroleum Geo-Services (PGS), Inc. in 2014 as a summer intern, where he worked on a semi-supervised seismic facies analysis. In 2017, he served as a postdoctoral Research Associate in the Attributed Assisted-Seismic Processing and Interpretation (AASPI) consortium at the University of Oklahoma from 2017 to 2020.

    Rocky R. Roden
    Senior Consulting Geophysicist

    The Relationship of Self-Organization, Geology, and Machine Learning

    Self-organization is the nonlinear formation of spatial and temporal structures, patterns or functions in complex systems (Aschwanden et al., 2018). Simple examples of self-organization include flocks of birds, schools of fish, crystal development, formation of snowflakes, and fractals. What these examples have in common is the appearance of structure or patterns without centralized control. Self-organizing systems are typically governed by power laws, such as the Gutenberg-Richter law of earthquake frequency and magnitude. In addition, the time frames of such systems display a characteristic self-similar (fractal) response, where earthquakes or avalanches for example, occur over all possible time scales (Baas, 2002).

    The existence of nonlinear dynamic systems and ordered structures in the earth are well known and have been studied for centuries and can appear as sedimentary features, layered and folded structures, stratigraphic formations, diapirs, eolian dune systems, channelized fluvial and deltaic systems, and many more (Budd, et al., 2014; Dietrich and Jacob, 2018). Each of these geologic processes and features exhibit patterns through the action of undirected local dynamics and is generally termed “self-organization” (Paola, 2014).

    Artificial intelligence and specifically neural networks exhibit and reveal self-organization characteristics. The reason for the interest in applying neural networks stems from the fact that they are universal approximators for various kinds of nonlinear dynamical systems of arbitrary complexity (Pessa, 2008). A special class of artificial neural networks is aptly named self-organizing map (SOM) (Kohonen, 1982). It has been found that SOM can identify significant organizational structure in the form of clusters from seismic attributes that relate to geologic features (Strecker and Uden, 2002; Coleou et al., 2003; de Matos, 2006; Roy et al., 2013; Roden et al., 2015; Zhao et al., 2016; Roden et al., 2017; Zhao et al., 2017; Roden and Chen, 2017; Sacrey and Roden, 2018; Leal et al, 2019; Hussein et al., 2020; Hardage et al., 2020; Manauchehri et al., 2020). As a consequence, SOM is an excellent machine learning neural network approach utilizing seismic attributes to help identify self-organization features and define natural geologic patterns not easily seen or seen at all in the data.

    Rocky R. Roden
    Senior Consulting Geophysicist

    Rocky R. Roden started his own consulting company, Rocky Ridge Resources Inc. in 2003 and works with several oil companies on technical and prospect evaluation issues. He is also a principal in the Rose and Associates DHI Risk Analysis Consortium and was Chief Consulting Geophysicist with Seismic Micro-technology. Rocky is a proven oil finder with 37 years in the industry, gaining extensive knowledge of modern geoscience technical approaches.

    Rocky holds a BS in Oceanographic Technology-Geology from Lamar University and a MS in Geological and Geophysical Oceanography from Texas A&M University. As Chief Geophysicist and Director of Applied Technology for Repsol-YPF, his role comprised of advising corporate officers, geoscientists, and managers on interpretation, strategy and technical analysis for exploration and development in offices in the U.S., Argentina, Spain, Egypt, Bolivia, Ecuador, Peru, Brazil, Venezuela, Malaysia, and Indonesia. He has been involved in the technical and economic evaluation of Gulf of Mexico lease sales, farmouts worldwide, and bid rounds in South America, Europe, and the Far East. Previous work experience includes exploration and development at Maxus Energy, Pogo Producing, Decca Survey, and Texaco. Rocky is a member of SEG, AAPG, HGS, GSH, EAGE, and SIPES; he is also a past Chairman of The Leading Edge Editorial Board.

    Bob A. Hardage

    Bob A. Hardage received a PhD in physics from Oklahoma State University. His thesis work focused on high-velocity micro-meteoroid impact on space vehicles, which required trips to Goddard Space Flight Center to do finite-difference modeling on dedicated computers. Upon completing his university studies, he worked at Phillips Petroleum Company for 23 years and was Exploration Manager for Asia and Latin America when he left Phillips. He moved to WesternAtlas and worked 3 years as Vice President of Geophysical Development and Marketing. He then established a multicomponent seismic research laboratory at the Bureau of Economic Geology and served The University of Texas at Austin as a Senior Research Scientist for 28 years. He has published books on VSP, cross-well profiling, seismic stratigraphy, and multicomponent seismic technology. He was the first person to serve 6 years on the Board of Directors of the Society of Exploration Geophysicists (SEG). His Board service was as SEG Editor (2 years), followed by 1-year terms as First VP, President Elect, President, and Past President. SEG has awarded him a Special Commendation, Life Membership, and Honorary Membership. He wrote the AAPG Explorer column on geophysics for 6 years. AAPG honored him with a Distinguished Service award for promoting geophysics among the geological community.

    Bob A. Hardage

    Investigating the Internal Fabric of VSP data with Attribute Analysis and Unsupervised Machine Learning

    Examination of vertical seismic profile (VSP) data with unsupervised machine learning technology is a rigorous way to compare the fabric of down-going, illuminating, P and S wavefields with the fabric of up-going reflections and interbed multiples created by these wavefields. This concept is introduced in this paper by applying unsupervised learning to VSP data to better understand the physics of P and S reflection seismology. The zero-offset VSP data used in this investigation were acquired in a hard-rock, fast-velocity, environment that caused the shallowest 2 or 3 geophones to be inside the near-field radiation zone of a vertical-vibrator baseplate. This study shows how to use instantaneous attributes to backtrack down-going direct-P and direct-S illuminating wavelets to the vibrator baseplate inside the near-field zone. This backtracking confirms that the points-of-origin of direct-P and direct-S are identical. The investigation then applies principal component (PCA) analysis to VSP data and shows that direct-S and direct-P wavefields that are created simultaneously at a vertical-vibrator baseplate have the same dominant principal components. A self-organizing map (SOM) approach is then taken to illustrate how unsupervised machine learning describes the fabric of down-going and up-going events embedded in vertical-geophone VSP data. These SOM results show that a small number of specific neurons build the down-going direct-P illuminating wavefield, and another small group of neurons build up-going P primary reflections and early-arriving down-going P multiples. The internal attribute fabric of these key down-going and up-going neurons are then compared to expose their similarities and differences. This initial study indicates that unsupervised machine learning, when applied to VSP data, is a powerful tool for understanding the physics of seismic reflectivity at a prospect. This research strategy of analyzing VSP data with unsupervised machine learning will now expand to horizontal-geophone VSP data.

    Tom Smith
    President and CEO, Geophysical Insights

    Machine Learning for Incomplete Geoscientists

    This presentation covers big-picture machine learning buzz words with humor and unassailable frankness. The goal of the material is for every geoscientist to gain confidence in these important concepts and how they add to our well-established practices, particularly seismic interpretation. Presentation topics include a machine learning historical perspective, what makes it different, a fish factory, Shazam, comparison of supervised and unsupervised machine learning methods with examples, tuning thickness, deep learning, hard/soft attribute spaces, multi-attribute samples, and several interpretation examples. After the presentation, you may not know how to run machine learning algorithms, but you should be able to appreciate their value and avoid some of their limitations.

    Deborah Sacrey
    Owner, Auburn Energy

    Deborah is a geologist/geophysicist with 44 years of oil and gas exploration experience in Texas, Louisiana Gulf Coast and Mid-Continent areas of the US. She received her degree in Geology from the University of Oklahoma in 1976 and immediately started working for Gulf Oil in their Oklahoma City offices.

    She started her own company, Auburn Energy, in 1990 and built her first geophysical workstation using Kingdom software in 1996. She helped SMT/IHS for 18 years in developing and testing the Kingdom Software. She specializes in 2D and 3D interpretation for clients in the US and internationally. For the past nine years she has been part of a team to study and bring the power of multi-attribute neural analysis of seismic data to the geoscience public, guided by Dr. Tom Smith, founder of SMT. She has become an expert in the use of Paradise software and has seven discoveries for clients using multi-attribute neural analysis.

    Deborah has been very active in the geological community. She is past national President of SIPES (Society of Independent Professional Earth Scientists), past President of the Division of Professional Affairs of AAPG (American Association of Petroleum Geologists), Past Treasurer of AAPG and Past President of the Houston Geological Society. She is also Past President of the Gulf Coast Association of Geological Societies and just ended a term as one of the GCAGS representatives on the AAPG Advisory Council. Deborah is also a DPA Certified Petroleum Geologist #4014 and DPA Certified Petroleum Geophysicist #2. She belongs to AAPG, SIPES, Houston Geological Society, South Texas Geological Society and the Oklahoma City Geological Society (OCGS).

    Mike Dunn
    Senior Vice President Business Development

    Michael A. Dunn is an exploration executive with extensive global experience including the Gulf of Mexico, Central America, Australia, China and North Africa. Mr. Dunn has a proven a track record of successfully executing exploration strategies built on a foundation of new and innovative technologies. Currently, Michael serves as Senior Vice President of Business Development for Geophysical Insights.

    He joined Shell in 1979 as an exploration geophysicist and party chief and held increasing levels or responsibility including Manager of Interpretation Research. In 1997, he participated in the launch of Geokinetics, which completed an IPO on the AMEX in 2007. His extensive experience with oil companies (Shell and Woodside) and the service sector (Geokinetics and Halliburton) has provided him with a unique perspective on technology and applications in oil and gas. Michael received a B.S. in Geology from Rutgers University and an M.S. in Geophysics from the University of Chicago.

    Hal GreenDirector, Marketing & Business Development - Geophysical Insights

    Hal H. Green is a marketing executive and entrepreneur in the energy industry with more than 25 years of experience in starting and managing technology companies. He holds a B.S. in Electrical Engineering from Texas A&M University and an MBA from the University of Houston. He has invested his career at the intersection of marketing and technology, with a focus on business strategy, marketing, and effective selling practices. Mr. Green has a diverse portfolio of experience in marketing technology to the hydrocarbon supply chain – from upstream exploration through downstream refining & petrochemical. Throughout his career, Mr. Green has been a proven thought-leader and entrepreneur, while supporting several tech start-ups.

    He started his career as a process engineer in the semiconductor manufacturing industry in Dallas, Texas and later launched an engineering consulting and systems integration business. Following the sale of that business in the late 80’s, he joined Setpoint in Houston, Texas where he eventually led that company’s Manufacturing Systems business. Aspen Technology acquired Setpoint in January 1996 and Mr. Green continued as Director of Business Development for the Information Management and Polymer Business Units.

    In 2004, Mr. Green founded Advertas, a full-service marketing and public relations firm serving clients in energy and technology. In 2010, Geophysical Insights retained Advertas as their marketing firm. Dr. Tom Smith, President/CEO of Geophysical Insights, soon appointed Mr. Green as Director of Marketing and Business Development for Geophysical Insights, in which capacity he still serves today.

    Hana Kabazi
    Product Manager

    Hana Kabazi joined Geophysical Insights in October of 201, and is now one of our Product Managers for Paradise. Mrs. Kabazi has over 7 years of oil and gas experience, including 5 years and Halliburton – Landmark. During her time at Landmark she held positions as a consultant to many E&P companies, technical advisor to the QA organization, and as product manager of Subsurface Mapping in DecsionSpace. Mrs. Kabazi has a B.S. in Geology from the University of Texas Austin, and an M.S. in Geology from the University of Houston.

    Dr. Carrie LaudonSenior Geophysical Consultant - Geophysical Insights

    Carolan (Carrie) Laudon holds a PhD in geophysics from the University of Minnesota and a BS in geology from the University of Wisconsin Eau Claire. She has been Senior Geophysical Consultant with Geophysical Insights since 2017 working with Paradise®, their machine learning platform. Prior roles include Vice President of Consulting Services and Microseismic Technology for Global Geophysical Services and 17 years with Schlumberger in technical, management and sales, starting in Alaska and including Aberdeen, Scotland, Houston, TX, Denver, CO and Reading, England. She spent five years early in her career with ARCO Alaska as a seismic interpreter for the Central North Slope exploration team.