Relating Seismic Interpretation to Reserve / Resource Calculations

By Rocky Roden and Mike Forrest, Rose & Associates, Roger Holeywell, Marathon Oil Company | Published with permission: The Leading Edge | September 2012

Abstract

In the process of quantifying resources/reserves, geoscientists attempt to employ all the available pertinent information to produce the most accurate results. The presence of direct hydrocarbon indicators (DHI) on seismic data can have a significant impact on the reserve/resource calculations not only for volumes, but also uncertainty levels. In 2001 a consortium of oil companies was organized in an attempt to understand seismic amplitude anomalies interpreted as DHIs and their impact on prospect risking and resource calculations (Roden et al., 2005; Forrest et al., 2010). The geologic setting, seismic and rock physics data quality, DHI characteristics, and calibration of drilling results are all incorporated into a database in a consistent and systematic process. From this process, the evaluation of 217 prospects and associated well results has enabled an in-depth understanding of the relevant key aspects of seismic amplitude anomalies and how they relate to drilling results.

DHI Consortium background

In the DHI Consortium, 41 oil companies have contributed to developing a seismic amplitude analysis process that systematically provides a probability of geologic success (Pg) values and associated implications for resource evaluations (Figure 1). The goals of the DHI consortium over the past 11 years have been and continue to be the following:

  1. Gain a better understanding of how seismic amplitudes impact prospect risking (i.e., predrill chance of geological success or Pg).
  2. Objectively characterize DHI observations using documented occurrences of recoverable hydrocarbons in the subsurface via prospect reviews and risk analysis discussions.
  3. Archive a statistically significant library of drilled prospect results.
  4. Use the prospect database to improve the predictability of Pg and range of uncertainty of hydrocarbon volumes.
  5. Improve the DHI work analysis process to help risk seismic amplitudes and provide an educational tool for interpreters.
  6. Discussion and review of pertinent technologies for amplitude interpretation.

 

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These companies have supplied 217 prospects from around the world with approximately an equal number of successes, dry holes, and objective reservoirs ranging in age from Triassic to Pleistocene. The prospects range in size from less than 100 acres to greater than 10,000 acres and depths from 2000 to more than 20,000 ft. This database is predominantly made up of exploratory wells with only 14% coming from “development” or “extension to known reservoir” wells. The closure types include structural (59%), stratigraphic (26%), and combination structural/stratigraphic (15%) traps.

Critical in the evaluation of DHI characteristics is an understanding of the geologic setting. Because expected DHI responses vary depending on the geologic setting, the prospects in the database are categorized by AVO classes 1–4 as displayed in Figure 4 (Rutherford and Williams, 1989; Castagna et al., 1998). However, 76% of the prospects are class 3 and 22% are class 2, with only a few from class 1 and 4 settings.

A general description of the input information and quantified output for this database as is described in Table 1.

quanitifed workflow
Table 1. General input and quantified output from DHI analysis process. Note that the input parameters are not necessarily linked directly to the corresponding quantified output row.

Well locations for the DHI Consortium
Figure 2. Locations around the world of the 217 wells in DHI Consortium database

There are several ways the most important DHI characteristics can be determined from the well results. The following list is a result of when a DHI characteristic was rated to be a 4 or 5 on a scale of 1–5, with 1 being the worst and 5 the best. These results were then correlated with whether the wells were successful or dry holes. This approach eliminates any scaling or weighting effects and compares only whether the DHI characteristic is strongly correlated with the actual postdrill well results. The top five DHI characteristics have been determined for both class 2 and 3 AVO categories, which relate to different geologic settings, and for which there are a sufficient number of example prospects to be meaningful.

seismic interpretation prospect types
Figure 3. Prospect types of wells in the DHI Consortium database.

A class 3 AVO setting relates to an unconsolidated gas sand with a gross interval velocity usually less than 8500 ft/s (2650 m/s). These reservoirs typically are low-impedance sands encased in relatively higher-impedance shales. Gas sands in this setting exhibit high amplitudes on the stack section as well as all offsets and angles. The seismic response from the top of these reservoirs produces high amplitudes with respect to background and increase (at times only slightly) with increasing offset/angle (Figure 4).

Top five class 3 DHI characteristics
  1. Amplitude downdip conformance (fit to closure) on stacked or far offset seismic data. This is the top-rated DHI characteristic in the database and describes how well the seismic amplitudes of the anomaly conform to the downdip structural contours of the reservoir. Key issues are whether the structural contours are in time or depth, potential velocity variations that can affect imaging, the accuracy of how the amplitudes were picked (top, bottom, or window) at the anomaly, and consideration of any stratigraphic variations. It is important to remember that this characteristic relates only to the downdip edge of the amplitude anomaly and how it compares to the interpreted structure. It does not relate to an amplitude’s conformance on the flanks of stratigraphic traps. This lateral conformance characteristic in stratigraphic traps depends on the geologic model and the strength of the independent evidence that supports that model.
  2. Phase or character change at the downdip edge of the anomaly. This DHI characteristic relates to a change in character such as phase or frequency at the downdip edge of the anomaly. As the model in Figure 6 displays, the transition from the hydrocarbon leg to the water leg in a sand can produce a seismic response indicative of the edge of a reservoir.
  3. Amplitude consistency in the mapped target area. The internal consistency of the seismic amplitude in a reservoir was found to be a significant DHI characteristic. This relates to the uniformity of the amplitude response within the mapped target area as interpreted from the stacked seismic data (Figure 7). When evaluating this characteristic, consideration should be given to possible faulting and stratigraphic changes that may modify internal consistency.

amplitude change with offset
Figure 4. (top) AVO classes based on amplitude change with offset from the top of gas sands (Rutherford and Williams, 1989; Castagna et al., 1998). (bottom) Angle gathers, zero-offset, and stack responses for typical class 2 and 3 AVO classes.

  1. Flat spots. This characteristic represents the seismic response at a hydrocarbon contact that presumably is relatively flat. This contact can be at the gas/oil contact, oil/ water contact, or gas/water contact. The gross reservoir thickness of the hydrocarbon unit must be greater than the tuning thickness (vertical resolution) to image a flat spot. Extreme care must be taken because the base or edge of channels, low-angle faults, diagenetic boundaries, or even processing artifacts are often misinterpreted as flat spots. Low-saturation gas in a reservoir can also produce a flat spot and this possibility is usually assessed by regional geologic studies.
  2. Excluding possible stacked pays, the AVO response is anomalous compared to events above and below. On prestack data, usually gathers, this characteristic refers to whether there are high-amplitude events above and below the targeted anomaly that look similar (Figure 8). The reasoning is that the anomaly is relatively unique suggesting a hydrocarbon-bearing reservoir.

A class 2 setting contains gas sands more consolidated than class 3 sands with gross interval velocities usually between 8500 ft/s and 12,000 ft/s (2650 m/s and 3650 m/s). The acoustic impedances of these gas sands and encasing shales are about equal. The intercept or near-offset amplitude can vary from a weak positive to a weak negative. The AVO effect can be strongly more negative with offset (large gradient) in these settings (Figure 4).

Top five class 2 DHI characteristics
  1. Amplitude downdip conformance (fit to closure) on far-offset seismic data. The principles behind this characteristic are the same as the top characteristic for class 3 settings, but for class 2 prospects this is usually evaluated on the far offset seismic data.
  2. Consistency in mapped target area (typically on gathers, faroffset/angle stacks, or windowed attributes). Internal consistency of the seismic amplitude identified on far-offset data was found to be an important DHI characteristic for class 2 rocks just as it is for class 3 rocks.
  3. AVO observations using gathers, far-offset/angle stacks, or windowed attributes. This characteristic relates to an interpreter’s confidence that the AVO response is proper for a class 2 setting. In other words, the near-offset is low in amplitude (small peak or trough) and the amplitude increases in negative amplitude with offset. (Figure 4) Noisy gathers, incorrect NMO corrections, multiples, insufficient offset during acquisition, and processing artifacts often complicate evaluation of this characteristic.
  4. The AVO event is anomalous compared to the same event outside the closure. This characteristic describes whether the class 2 AVO response is unique compared to the correlative event outside the closure. This characteristic is often interpreted from far-offset/angle stacks, as well as, intercept, gradient, intercept x-gradient, far-near, and (farnear) x-far displays.
  5. Change in AVO compared to model (wet versus hydrocarbon- filled). Modeling of the AVO response, usually applying Gassman’s equation, typically involves substitution modeling of gas, oil, and water responses. A comparison of the in-situ and modeled responses to actual gathers provides confidence that hydrocarbons are present or not.

amplitude conformance
Figure 5. Examples of amplitude conformance to the downdip structural contours from grade 1 (worst) to grade 5 (best).

Reasons for failure

reservoir hydrocarbon and water logs
Figure 6. Model of hydrocarbon and water legs of a reservoir and location of possible phase or frequency change at edge of fluid contact (courtesy Quantum Earth Corp).

The consortium defines successful wells as geologic successes (i.e., wells with flowable hydrocarbons). However, in this database all successes were determined to also be commercially successful except for a few wells. These exceptions contained from 50 to more than 100 ft of gas, but were determined to be noneconomic because of their location in the world and lack of infrastructure. Wells containing only low-saturation gas are considered by the consortium to be dry holes even though they may represent successful predictions of acoustic response.

Wet sands in the target interval accounted for 49% of the dry holes in the database. Nearly half of these wet sands were thick. In fact, these thick wet sands were greater than the predrill P10 estimates for reservoir thickness. Hard shale on top of a wet sand, blocky sands with higher than expected porosities, and tuning effects of wet sands all produced amplitude anomalies misinterpreted to be direct hydrocarbon effects.

Low-saturation gas (LSG) accounted for 23% for the failures in the database. This well known phenomenon is caused by a small percentage of residual gas in a reservoir (5–10%) producing an acoustic effect similar to commercial saturation and is usually associated with a breach or break in reservoir seal. All but one of the LSG dry holes were in class 3 rocks, with one in a class 2 setting. All but one of the LSG dry holes were in normally pressured sediment columns. Four of the LSG wells exhibited acoustic flat spots indicative of a paleo hydrocarbon contact. Low vertical effective stress caused by undercompaction accounted for several LSG results in deepwater settings, but was also found where a relatively young sediment column overlaid the amplitude anomaly. Even though not dry holes, several wells exhibited hydrocarbons, usually gas, overlying a LSG column in the sand.

No reservoir present was responsible for 17% of the dry holes. Amplitudes misinterpreted to be hydrocarbons included low-density shale, ash, coal beds, top of hard overpressures, seismic processing artifacts, mud volcano, and diagenetic boundary.

Tight reservoirs accounted for 11% of the dry holes in the database. Examples of these include oil-charged marl, gas-charged condensed section, low-impedance siltstone/mudstone and low-permeability reservoir.

DHI anomaly
Figure 7. Examples of amplitude consistency within a defined DHI anomaly from grade 1 (worst) to grade 5 (best).

Implications for resource calculations in exploration

The presence of direct hydrocarbon indicators can have a significant impact on methods to compute prospective resources, especially in exploration settings. How do DHIs change area determination for resources? How do DHIs impact thickness determinations in resource computations? How does a seismic-amplitude (DHI) defined area and thickness relate to a geologically defined area and thickness? The presence of true direct hydrocarbon indicators indicates that all geologic chance factors (e.g., source, migration, seal, reservoir, and trap) are working to form a petroleum system; therefore, the critical issue is determining the confidence level that the seismic is truly displaying a direct hydrocarbon indicator because not all anomalous amplitude events are DHIs.

AVO anomaly
Figure 8. An unmuted CDP gather displaying an AVO anomaly that appears prominent compared to the events above and below (Roden et al., 2005).The near vertical lines represent offset angles (purple = 10°, red = 20°, green = 30°).

DHI Index
Figure 9. Illustration of DHI Index method to define and weight geologic and amplitude areas for resource calculations

For area determinations, one approach is to consider two separate distribution estimates. One distribution is based on geologic evidence, independent of the amplitude as a DHI, while the other distribution is based on the amplitude-defined area (Figure 9). Each distribution can be defined by a P10 (largest reasonable) and a P90 (smallest reasonable) value. The variance or P10/P90 ratio of the area is often large for the geologic distribution, but much smaller for the amplitude-defined distribution because the anomaly usually has a sharp cutoff. From statistics based on the DHI consortium results, a “DHI Index” has been calculated that indicates the likelihood that the interpreted amplitude anomaly is truly a direct hydrocarbon indicator. This “DHI Index” can be used as a relative weighting factor for the two distribution estimates.

seismic interpretation software - DHI Index
Figure 10. DHI Index values color-coded by drilling outcomes. Note that above 20% almost all wells are successful. There are negative DHI Index values, usually associated with dry holes, where DHI characteristics should be present, but were absent.

The histograms of Figure 10 show that, when the DHI Index is more than 20%, essentially all wells are successful. To get a DHI Index of 20% or higher requires having numerous positive DHI characteristics. Therefore, using this logic, prospects with a DHI Index of 20% or larger employ the DHI-defined area, whereas, a DHI Index of 0% or less employ the geologically defined area (Figure 9). A DHI Index of 0% or less indicates there is no DHI element at all in the area determination. When a prospect has a DHI Index between 0% and 20%, the area is defined by a simple linear weighting (e.g., a DHI Index of 10% would have a 50/50 weighting of geology and amplitude-defined areas) between the two distribution estimates.

For thickness determinations, similar to the DHI Index approach for area, two thickness distributions can be estimated. One is defined geologically and one by the amplitude-de- fined thickness. The geologically defined distribution should be based on well control, sand studies, depositional trends, and usually involves constructing regional and local sand isopachs consistent with the depositional model in terms of reservoir shape and thickness. From the combination of sand isopachs, depositional environment determinations, models and stratigraphy studies, an estimate of the P10 and P90 average net pay thicknesses can be calculated.

For the amplitude-defined thickness, the interpreter first needs to determine whether the anomaly is above tuning, below tuning, or both. Above tuning, the thickness is a function of the time separation between the top and bottom of the anomaly. Gross pay maps (isochrons) can be generated for the P10 and P90 cases including any geometric factors. An estimated net-to-gross and interval velocity are applied to determine the final thickness distributions. Below tuning, in the ideal situation, the composite amplitude from the top and bottom of the bed decreases linearly with thickness starting from tuning thickness. The amplitude should be calibrated with well control and stratigraphy studies. Below tuning, a starting point P10 could be the tuning thickness and a P90 could be the detection limit (approximately 1/30 wavelength of the dominant frequency). Appropriate interval velocities may need to be applied for thickness conversions. Below tuning, the composite amplitude usually incorporates any net-to-gross effects. Once a geologically defined thickness distribution and a seismic amplitude-defined thickness are determined, the DHI Index can be used to determine the weighting of these two distributions. It can be more difficult to separate and differentiate between a geologically and amplitude-defined thickness as compared to interpreting distributions for the area. In this situation, it may be more appropriate to combine geologic and amplitude thickness information and use one distribution without weighting to compute resources. Spectral decomposition has been found to be helpful in certain situations to help determine thickness trends.

Employing the DHI Index method to determine area and thickness for resource calculations is one approach that gives credit to both geologically and amplitude defined distributions. However, it is not uncommon in strong amplitude driven plays to employ the amplitude-defined distributions only, especially for area. The logic here is that the presence of amplitudes is defining the reservoir and the geologic distributions do not apply. Care should be taken in that all amplitudes are not DHIs and not all geologic settings exhibit DHIs equally. In addition, even if there is evidence that the area can be narrowly estimated, there will often be significant remaining uncertainty in resource size because of the variance in average net pay thickness and recovery factor estimates.

Implications for reserve calculations

The U. S. Securities and Exchange Commission (SEC) defines “proved oil and gas reserves” in part as “those quantities of oil and gas, which, by analysis of geosciences and engineering data, can be estimated with reasonable certainty to be economically producible— from a given date forward, from known reservoirs, and under existing economic conditions, operating methods, and government regulations — prior to the time at which contracts providing the right to operate expire, unless evidence indicates that renewal is reasonably certain, regardless of whether deterministic or probabilistic methods are used for the estimation.” Unlike resource calculations in exploration, reserve calculations have had a well or wells drilled to confirm geologic conditions and to some degree seismic amplitude features. Therefore the interpretation of DHI features relates to the determination of “reliable technology” as defined by the SEC. As defined by the SEC, “reliable technology is a grouping of one or more technologies (including computational methods) that has been field tested and has been demonstrated to provide reasonably certain results with consistency and repeatability in the formation being evaluated or in an analogous formation.” Therefore for DHI characteristics to qualify as reliable technology requires the correlation of amplitude features with well control, reservoir engineering tests, known geologic trends and stratigraphy.

Even with well control and substantial geologic control of thickness and area extents, care should be taken in incorporating DHI information from seismic data. Considerations for amplitude anomalies and reserve determinations include:

  1. Strong amplitude is primarily at wedge where tuning occurs, weaker amplitude updip
  2. Stratigraphic trap where conventional downdip and updip limits do not apply
  3. Reservoir thins updip, bald structure
  4. Velocity tilt can affect conformance to closure interpretations
  5. Faults affecting amplitudes for area determinations
  6. Stratigraphic changes across field
  7. Masking of amplitudes from bright spots above prospect
  8. Amplitude effects from low-saturation gas downdip of proven pay in the same reservoir
  9. Beware of large column heights for gas reservoirs
  10. Data issues, such as prospect at edge of survey, complicate area estimates

In development, seismic technologies such as spectral decomposition, neural networks, statistical approaches, and advanced seismic inversion techniques are often applied to help determine reserves. The transition from exploration to development and the use of seismic inversion requires the incorporation of all available geologic, seismic, and engineering data in an attempt to discriminate specifically between lithology, porosity, and fluid effects and how these parameters relate to reserve calculations.

Conclusions

Over the past 11 years, 41 oil companies have contributed data for the evaluation of more than 200 drilled prospects (including assessment of the associated technical information and drilling results). The subsequent database developed from these assessments has enabled the identification of the pertinent aspects of direct hydrocarbon indicators and their impact on risking and resource/reserve determination. This database is composed primarily of exploratory wells (86%) and has direct relevance for computing resources in exploration. However, the lessons learned from this database are also pertinent for reserve assessment in development because an understanding of the most important DHI characteristics, reasons for failure, and potential pitfalls in DHI interpretation provide valuable information in the reserve assessment process. Critical in the evaluation of DHI characteristics is the pertinent technical approaches employed and their calibration to known drilling results. However, even in development settings with a certain amount of well control for calibration, DHI interpretation requires a consistent and systematic evaluation procedure, including recognition of potential pitfalls and an understanding of the cause of the DHIs and their significance in reserve assessment.

References

Castagna, J., H. Swan, and D. Foster, 1998, Framework for AVO gradient and intercept interpretation: Geophysics, 63, no. 3, 948– 956.

Forrest, M., R. Roden, and R. Holeywell, 2010, Risking seismic amplitude anomaly prospects based on database trends: The Leading Edge, 29, 936–940.

Roden, R., M. Forrest, and R. Holeywell, 2005, The impact of seismic amplitudes on prospect risk analysis: The Leading Edge, 24, no. 7, 706–711.

Roden, R., J. Castagna, and G. Jones, 2005, The impact of prestack data phase on the AVO interpretation workflow-A case study: The Leading Edge, 24, 890–895.

Rutherford, S. E. and R. H. Williams, 1989, Amplitude versus offset variation in gas sands: Geophysics, 54, no. 6, 680–688.

U. S. Securities and Exchange Commission, 2008, Modernization of oil and gas reporting, December 31.

Acknowledgments: The authors thank the member companies of the DHI Consortium for providing invaluable information necessary to develop the resulting interpretation process and prospect database.

 

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    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 Green
    Director – Marketing & Business Development

    Introduction to the Paradise AI Workbench

    Companies worldwide are seeking solutions for their digital transformation initiatives and face a make-vs-buy decision when it comes to their E&P software tools. This talk will show how the commercial, off-the-shelf Paradise AI workbench can be a robust and cost-effective component of the new digital infrastructure. Using 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:

    • - Identify and calibrate detailed stratigraphy
    • - Distinguish thin beds below conventional tuning
    • - Classify seismic facies
    • - Detect faults automatically
    • - Interpret Direct Hydrocarbon Indicators
    • - Reveal fracture trends in shale plays
    • - Estimate reserves/resources

    The brief introduction includes single-slide use cases in different geologic settings to illustrate the general-purpose application of ‘AI’ technology. The summary also will provide some context to the other presentations available at the Geophysical Insights virtual booth.

    Hal Green
    Director of Marketing & Business Development

    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.

    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.

    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.

    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.

    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.

    Jie Qi
    Research Geophysicist

    Applications of Deep Learning-based Seismic Fault Detection

    The traditional fault detection method is based on geophysicists’ hand-picking, which is very time-consuming on large seismic datasets. Convolutional Neural Networks (CNN)-based fault detection method is an emerging technology that shows great promise for the seismic interpreter. One of the more successful deep learning CNN methods uses synthetic data to train a CNN model. Faults are labeled as a single classification and other background geologic features are another classification in CNN-based fault detection. The labeled faults with associated seismic amplitude data are used to train in a CNN model, then predict or classify the corresponding fault classification in a large seismic dataset by the trained CNN model. The outperformance of CNN-based methods is that the computation cost of applications of a pre-trained CNN model to seismic fault classification is extremely low. This study shows applications of CNN models to predict faults from 3D seismic data. Firstly, the CNN model is trained with multiple 3D synthetic seismic amplitude data and their associated fault label data. The training data has been considered with different data quality, frequency bandwidth, noise levels, and structural features. The well-trained CNN model is then applied to detect faults on datasets, which exhibit different noise level and geologic features. Then the results from CNN are compared to those obtained using traditional seismic attributes and manual interpretation. The comparison indicates that the CNN method can perform more accurately and has a high potential to do more on seismic fault detection.

    Jie Qi
    Research Geophysicist

    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.

    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.

    Laura Cuttill
    Practice Lead, Advertas

    Young Professionals – Managing Your Personal Brand to Level-up Your Career

    No matter where you are in your career, your online “personal brand” has a huge impact on providing opportunity for prospective jobs and garnering the respect and visibility needed for advancement. While geoscientists tackle ambitious projects, publish in technical papers, and work hard to advance their careers, often, the value of these isn’t realized beyond their immediate professional circle. Learn how to…

    • - 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.

    Mike Dunn
    Sr. Vice President of Business Development

    New Capabilities of 3.4

    Paradise has given interpreters the ability detect more detail within the seismic data. Therefore, a natural extension of the current software is the ability to easily compare the SOM and Geobody results to borehole logs and lithofacies. As a result of this exciting capability, Paradise is now able to display digital well logs, TD charts, formation tops, and cross-sections in simple and straightforward manner. In this What’s New in Paradise 3.4 presentation we will be discussing the new Well Log Cross Section functionality, GPU support for 3 AASPI algorithms, demonstrating significant speedup, and the latest Petrel 2020 connector. Examples of the new well functionality will use the offshore New Zealand Maui Field data set. In addition, a live demonstration will walk users through a well cross section workflow.

    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.

    Rocky R. Roden
    Senior Consulting Geophysicist

    What Interpreters Should Know about Machine Learning

    Our lives are intertwined with applications, services, orders, products, research, and objects that are incorporated, produced, or effected in some way by Artificial Intelligence and Machine Learning. Buzz words like Deep Learning, Big Data, Supervised and Unsupervised Learning are employed routinely to describe Machine Learning, but how do these applications relate to geoscience interpretation and finding oil and gas. More importantly, do these Machine Learning methods produce better results than conventional interpretation approaches? This webinar will initially wade through the vernacular of Machine Learning and Data Science as it relates to the geoscientist. The presentation will review how these methods are employed, along with interpretation case studies of different machine learning applications. An overview of computer power and machine learning will be described. Machine Learning is a disruptive technology that holds great promise, and this webinar is an interpreter’s perspective, not a data scientist. This course will provide an understanding of how Machine Learning for interpretation is being utilized today and provide insights on future directions and trends.

    Rocky R. Roden
    Senior Consulting Geophysicist

    Over 45 years in industry as a Geophysicist, Exploration/Development Manager, Director of Applied Technology, and Chief Geophysicist. Previously with Texaco, Pogo Producing, Maxus Energy, YPF Maxus, and Repsol (retired as Chief Geophysicist 2001). Mr. Roden has authored or co-authored over 30 technical publications on various aspects of seismic interpretation, AVO analysis, amplitude risk assessment, and geoscience machine learning. Ex-Chairman of The Leading Edge editorial board. Currently a consultant with Geophysical Insights developing machine learning advances for oil and gas exploration and development and is a principal in the Rose and Associates DHI Risk Analysis Consortium, which has involved 85 oil companies since 2001, developing a seismic amplitude risk analysis program and worldwide prospect database. He holds a B.S. in Oceanographic Technology-Geology from Lamar University and an M.S. in Geological and Geophysical Oceanography from Texas A&M University.

    Sarah Stanley
    Senior Geoscientist

    New Capabilities of 3.4

    Paradise has given interpreters the ability detect more detail within the seismic data. Therefore, a natural extension of the current software is the ability to easily compare the SOM and Geobody results to borehole logs and lithofacies. As a result of this exciting capability, Paradise is now able to display digital well logs, TD charts, formation tops, and cross-sections in simple and straightforward manner. In this What’s New in Paradise 3.4 presentation we will be discussing the new Well Log Cross Section functionality, GPU support for 3 AASPI algorithms, demonstrating significant speedup, and the latest Petrel 2020 connector. Examples of the new well functionality will use the offshore New Zealand Maui Field data set. In addition, a live demonstration will walk users through a well cross section workflow.

    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.

    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.

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