The science of petroleum geophysics is changing, driven by the nature of the technical and business demands facing geoscientists as oil and gas activity pivots toward a new phase of unconventional reservoir development in an economic environment that rewards efficiency and risk mitigation. At the same time, fast-evolving technologies such as machine learning and multiattribute data analysis are introducing powerful new capabilities in investigating and interpreting the seismic record.
Through it all, however, the core mission of the interpreter remains the same as ever: extracting insights from seismic data to describe the subsurface and predict geology between existing well locations–whether they are separated by tens of feet on the same horizontal well pad or tens of miles in adjacent deepwater blocks. Distilled to its fundamental level, the job of the data interpreter is to determine where (and where not) to drill and complete a well. Getting the answer correct to that million-dollar question gives oil and gas companies a competitive edge. The ability to arrive at the right answers in the timeliest manner possible is invariably the force that pushes technological boundaries in seismic imaging and interpretation. The state of the art in seismic interpretation is being redefined partly by the volume and richness of high-density, full-azimuth 3-D surveying methods and processing techniques such as reverse time migration and anisotropic tomography. Combined, these solutions bring new resolution and clarity to processed subsurface images that simply are unachievable using conventional imaging methods. In data interpretation, analytical tools such as machine learning, pattern recognition, multiattribute analysis and self-organizing maps are enhancing the interpreter’s ability to classify, model and manipulate data in multidimensional space. As crucial as the technological advancements are, however, it is clear that the future of petroleum geophysics is being shaped largely by the demands of North American unconventional resource plays. Optimizing the economic performance of tight oil and shale gas projects is not only impacting the development of geophysical technology, but also dictating the skill sets that the next generation of successful interpreters must possess. Resource plays shift the focus of geophysics to reservoir development, challenging the relevance of seismic-based methods in an engineering-dominated business environment. Engineering holds the purse strings in resource plays, and the problems geoscientists are asked to solve with 3-D seismic are very different than in conventional exploration geophysics. Identifying shallow drilling hazards overlying a targeted source rock, mapping the orientation of natural fractures or faults, and characterizing changes in stress profiles or rock properties is related as much to engineering as to geophysics.
Given the requirements in unconventional plays, there are four practical steps to creating value with seismic analysis methods. The first and obvious step is for oil and gas companies to acquire 3-D seismic and incorporate the data into their digital databases. Some operators active in unconventional plays fully embrace 3-D technology, while others only apply it selectively. If interpreters do not have access to high-quality data and the tools to evaluate that information, they cannot possibly add value to company’s bottom line.The second step is to break the conventional resolution barrier on the seismic reflection wavelet, the so-called quarter-wave length limit. This barrier is based on the overlapping reflections of seismic energy from the top and bottom of a layer, and depends on layer velocity, thickness, and wavelet frequencies. Below the quarter-wave length, the wavelets start to overlap in time and interfere with one another, making it impossible by conventional means to resolve separate events. The third step is correlating seismic reflection data–including compressional wave energy, shear wave energy and density–to quantitative rock property and geomechanical information from geology and petrophysics. Connecting seismic data to the variety of very detailed information available at the borehole lowers risk and provides a clearer picture of the subsurface between wells, which is fundamentally the purpose of acquiring a 3-D survey. The final step is conducting a broad, multiscaled analysis that fully integrates all available data into a single rock volume encompassing geophysical, geologic and petrophysical features. Whether an unconventional shale or a conventional carbonate, bringing all the data together in a unified rock volume resolves issues in subsurface modeling and enables more realistic interpretations of geological characteristics.
The Role of Technology
Every company faces pressures to economize, and the pressures to run an efficient business only ratchet up at lower commodity prices. The business challenges also relate to the personnel side of the equation, and that should never be dismissed. Companies are trying to bridge the gap between older geoscientists who seemingly know everything and the ones entering the business who have little experience but benefit from mentoring, education and training. One potential solution is using information technology to capture best practices across a business unit, and then keeping a scorecard of those practices in a database that can offer expert recommendations based on past experience. Keylogger applications can help by tracking how experienced geoscientists use data and tools in their day-to-day workflows. However, there is no good substitute for a seasoned interpreter. Technologies such as machine learning and pattern recognition have game-changing possibilities in statistical analysis, but as petroleum geologist Wallace Pratt pointed out in the 1950s, oil is first found in the human mind. The role of computing technology is to augment, not replace, the interpreter’s creativity and intuitive reasoning (i.e., the “geopsychology” of interpretation).
A self-organizing map (SOM) is a neural network-based, machine learning process that is simultaneously applied to multiple seismic attribute volumes. This example shows a class II amplitude-variation-with-offset response from the top of gas sands, representing the specific conventional geological settings where most direct hydrocarbon indicator characteristics are found. From the top of the producing reservoir, the top image shows a contoured time structure map overlain by amplitudes in color. The bottom image is a SOM classification with low probability (less than 1 percent) denoted by white areas. The yellow line is the downdip edge of the high-amplitude zone designated in the top image. Consequently, seismic data interpreters need to make the estimates they derive from geophysical data more quantitative and more relatable for the petroleum engineer. Whether it is impedance inversion or anisotropic velocity modeling, the predicted results must add some measure of accuracy and risk estimation. It is not enough to simply predict a higher porosity at a certain reservoir depth. To be of consequence to engineering workflows, porosity predictions must be reliably delivered within a range of a few percentage points at depths estimated on a scale of plus or minus a specific number of feet.
Class II amplitude-variation-with-offset response from the top of gas sand.
Machine learning techniques apply statistics-based algorithms that learn iteratively from the data and adapt independently to produce repeatable results. The goal is to address the big data problem of interpreting massive volumes of data while helping the interpreter better understand the interrelated relationships of different types of attributes contained within 3-D data. The technology classifies attributes by breaking data into what computer scientists call “objects” to accelerate the evaluation of large datasets and allow the interpreter to reach conclusions much faster. Some computer scientists believe “deep learning” concepts can be applied directly to 3-D prestack seismic data volumes, with an algorithm figuring out the relations between seismic amplitude data patterns and the desired property of interest. While Amazon, Alphabet and others are successfully using deep learning in marketing and other functions, those applications have access to millions of data interactions a day. Given the significantly fewer number of seismic interpreters in the world, and the much greater sensitivity of 3-D data volumes, there may never be sufficient access to training data to develop deep learning algorithms for 3-D interpretation.The concept of “shallow learning” mitigates this problem.
Conventional amplitude seismic display from a northwest-to-southeast seismic section across a well location is contrasted with SOM results using multiple instantaneous attributes.
First, 3-D seismic data volumes are converted to well-established relations that represent waveform shape, continuity, orientation and response with offsets and azimuths that have proven relations (“attributes”) to porosity, thickness, brittleness, fractures and/or the presence of hydrocarbons. This greatly simplifies the problem, with the machine learning algorithms only needing to find simpler (i.e., shallower) relations between the attributes and properties of interest.In resource plays, seismic data interpretations increasingly are based on statistical rather than deterministic predictions. In development projects with hundreds of wells within a 3-D seismic survey area, operators rely on the interpreter to identify where to drill and predict how a well will complete and produce. Given the many known and unknown variables that can impact drilling, completion and production performance, the challenge lies with figuring out how to use statistical tools to apply data measurements from the previous wells to estimate the performance of the next well drilled within the 3-D survey area. Therein lies the value proposition of any kind of science, geophysics notwithstanding. The value of applying machine learning-based interpretation boils down to one word: prediction. The goal is not to score 100 percent accuracy, but to enhance the predictions made from seismic analysis to avoid drilling uneconomic or underproductive wells. Avoiding investments in only a couple bad wells can pay for all the geophysics needed to make those predictions. And because the statistical models are updated with new data as each well is drilled and completed, the results continually become more quantitative for improved prediction accuracy over time.
In terms of particular interpretation functionalities, three specific concepts are being developed around machine learning capabilities:
Evaluating multiple seismic attributes simultaneously using self-organizing maps (multiattribute analysis);
Relating in multidimensional space natural clusters or groupings of attributes that represent geologic information embedded in the data; and
Graphically representing the clustered information as geobodies to quantify the relative contributions of each attribute in a given seismic volume in a form that is intrinsic to geoscientific workflows.
A 3-D seismic volume contains numerous attributes, expressed as a mathematical construct representing a class of data from simultaneous analysis. An individual class of data can be any measurable property that is used to identify geologic features, such as rock brittleness, total organic carbon or formation layering. Supported by machine learning and neural networks, multiattribute technology enhances the geoscientist’s ability to quickly investigate large data volumes and delineate anomalies for further analysis, locate fracture trends and sweet spots in shale plays, identify geologic and stratigraphic features, map subtle changes in facies at or even below conventional seismic resolution, and more. The key breakthrough is that the new technology works on machine learning analysis of multiattribute seismic samples.While applied exclusively to seismic data at present, there are many types of attributes contained within geologic, petrophysical and engineering datasets. In fact, literally, any type of data that can be put into rows and columns on a spreadsheet is applicable to multiattribute analysis. Eventually, multiattribute analysis will incorporate information from different disciplines and allow all of it to be investigated within the same multidimensional space that leads to the second concept: Using machine learning to organize and evaluate natural clusters of attribute classes. If an interpreter is analyzing eight attributes in an eight-dimensional space, the attributes can be grouped into natural clusters that populate that space. The third component is delivering the information found in the clusters in high-dimensionality space in a form that quantifies the relative contribution of the attributes to the class of data, such as simple geobodies displayed with a 2-D color index map. This approach allows multiple attributes to be mapped over large areas to obtain a much more complete picture of the subsurface, and has demonstrated the ability to achieve resolution below conventional seismic tuning thickness. For example, in an application in the Eagle Ford Shale in South Texas, multiattribute analysis was able to match 24 classes of attributes within a 150-foot vertical section across 200 square miles of a 3-D survey. Using these results, a stratigraphic diagram of the seismic facies has been developed over the entire survey area to improve geologic predictions between boreholes, and ultimately, correlate seismic facies with rock properties measured at the boreholes. Importantly, the mathematical foundation now exists to demonstrate the relationships of the different attributes and how they tie with pixel components in geobody form using machine learning. Understanding how the attribute data mathematically relate to one another and to geological properties gives geoscientists confidence in the interpretation results.
The term “exploration geophysics” is becoming almost a misnomer in North America, given the focus on unconventional reservoirs, and how seismic methods are being used in these plays to develop rather than find reservoirs. With seismic reflection data being applied across the board in a variety of ways and at different resolutions in unconventional development programs, operators are combining 3-D seismic with data from other disciplines into a single integrated subsurface model. Fully leveraging the new sets of statistical and analytical tools to make better predictions from integrated multidisciplinary datasets is crucial to reducing drilling and completion risk and improving operational decision making. Multidimensional classifiers and attribute selection lists using principal component analysis and independent component analysis can be used with geophysical, geological, engineering, petrophysical and other attributes to create general-purpose multidisciplinary tools of benefit to all oil and gas company departments and disciplines. As noted, the integrated models used in resource plays increasingly are based on statistics, so any evaluation to develop the models also needs to be statistical. In the future, a basic part of conducting a successful analysis will be the ability to understand statistical data and how the data can be organized to build more tightly integrated models. And if oil and gas companies require more integrated interpretations, it follows that interpreters will have to possess more integrated skills and knowledge. The geoscientist of tomorrow may need to be more of a multidisciplinary professional with the blended capabilities of a geologist, geophysicist, engineer and applied statistician. But whether a geoscientist is exploring, appraising or developing reservoirs, he or she only can be as good as the prediction of the final model. By applying technologies such as machine learning and multiattribute analysis during the workup, interpreters can use their creative energies to extract more knowledge from their data and make more knowledgeable predictions about undrilled locations.
THOMAS A. SMITH is president and chief executive officer of Geophysical Insights, which he founded in 2008 to develop machine learning processes for multiattribute seismic analysis. Smith founded Seismic Micro-Technology in 1984, focused on personal computer-based seismic interpretation. He began his career in 1971 as a processing geophysicist at Chevron Geophysical. Smith is a recipient of the Society of Exploration Geophysicists’ Enterprise Award, Iowa State University’s Distinguished Alumni Award and the University of Houston’s Distinguished Alumni Award for Natural Sciences and Mathematics. He holds a B.S. and an M.S. in geology from Iowa State, and a Ph.D. in geophysics from the University of Houston.
KURT J. MARFURT is the Frank and Henrietta Schultz Chair and Professor of Geophysics in the ConocoPhillips School of Geology & Geophysics at the University of Oklahoma. He has devoted his career to seismic processing, seismic interpretation and reservoir characterization, including attribute analysis, multicomponent 3-D, coherence and spectral decomposition. Marfurt began his career at Amoco in 1981. After 18 years of service in geophysical research, he became director of the University of Houston’s Center for Applied Geosciences & Energy. He joined the University of Oklahoma in 2007. Marfurt holds an M.S. and a Ph.D. in applied geophysics from Columbia University.
Permanent sensors both on land and on the seafloor are collecting a new stream of seismic data that can be used for repeated active seismic, microseismic analysis, and continuous passive monitoring. Distributed acoustic sensors (DAS) record continuous seismic data very cheaply, taking another quantum step in the amount of data coming from the reservoir during exploration, development and production.
These are just two examples of how dramatically the volume of technical data is rising, says Biondo Biondi, professor of geophysics at Stanford University. “The big change taking place is in the breadth of data we can get with different kinds of sensors,” he states. “Beyond seismic, there are streams of data from sensors measuring temperature, pressure, flow, and other physical information. This is putting a strain on computational capability, but it does open the possibility of a lot of integration of geophysical and other data.”
Data sources are evolving rapidly, becoming less expensive and providing denser data. “One Stanford student is experimenting with rotational sensors that record six or seven components,” says Biondi. “Others are working with both active and passive DAS data.”
When and how to process those data are also subjects of study. “A simple DAS fiber creates terabytes of data every day,” he explains. “It is unlikely that all of the data can move in bulk across the network. Instead, some amount of real-time processing will be needed near the source.”
In addition, while DAS arrays offer a low-cost way to collect dense acoustic data passively or actively, data quality is lower than from conventional geophones, Biondi says. “The challenge in this case is to get high-quality insight from low-quality data.”
While cloud computing certainly is proving useful in meeting some industry needs, Biondi says it may be more appropriate to keep the data “closer to the ground” because of its volume and proprietary nature. “Fog computing is the term for this mixed model,” he relates.
Data collected from DAS acquisition may be preprocessed local to the acquisition center, for example, then sent to the cloud for analysis, and then into the hands of the interpreter, he speculates. “The more channels and better data collected, the more accurate the wave field capture,” he comments. “This will speed the transition from seismic processing to waveform imaging. The interpreter and processor can interact in a feedback loop. Many types of geological and geophysical information could be part of the fog computing process.”
Another ongoing trend in geophysics is a push to place more emphasis on reservoir-centered geology, according to Biondi. “The goal is tighter integration of reservoir properties, geomechanics, seismic, petrophysics, etc. One student constrained anisotropic parameter estimation using petrophysical data and well logs and models. Some students are constraining attenuation and connecting seismic with geomechanics, including reservoir compaction and overburden stretching. Others are working with reservoir engineers to model fluid flows that include geomechanical effects,” he notes.
“As we move toward waveform inversion, we no longer are dealing with ‘magic’ processing parameters, but with more description of the geology,” says Biondi. “That allows us to bring quantitative information into seismic imaging.”
That includes unconventional plays, where Biondi says integrated reservoir analysis soon could be performed in real time to guide well planning, drilling, completion and fracturing design decisions.
An important step in data analysis is merging statistical data analytics with physics-based analysis. “Traditional seismic imaging is based on the physics of waveform propagation, fluid flow modeling is based on physics, and geomechanical analysis is based on mechanical modeling,” Biondi remarks. “By adding details about the physics and geology, we can point researchers in the direction of physical phenomenon or geological settings where a different understanding of the geology and physics is needed.”
Integrating Data And Processes
The industry is finding tremendous value in integrating data and multidisciplinary processes, says Kamal Al-Yahya, senior vice president at CGG GeoSoftware. Traditional tools for reservoir characterization and petrophysical analysis were essentially siloed by data type and discipline. Geophysicists worked with seismic data, geologists worked with petrophysical data, and drilling and reservoir departments worked with engineering data.
“The associated applications for each domain can be best in class, but workflows still can suffer from addressing only part of the data spectrum and serving only a segment of the different disciplines involved,” he observes. “Industry professionals would like to work together more to improve efficiency and build on one another’s ideas. That requires integration.”
Integration at the workflow level lets users access several applications in interpretation and design workflows without having to move data, he explains, referencing the example of a smart phone where contact data are used by many applications from a single source. “Users in various disciplines can begin to collaborate. Normally they have different perspectives,” Al-Yahya says. “Everybody can be looking at the same data, but users in each discipline will see them differently based on their areas of expertise.”
While upstream software applications tend to be highly scientific and complex, Al-Yahya says new computing technologies are making applications easier to use. “A complex application does not have to have a complex interface,” he holds. “Simpler interfaces support collaboration between geographically dispersed experts and across disciplines.”
Automation is an important step toward reducing interface complexities. Al-Yahya points out that processing algorithms at the front end of seismic analysis have automated removing survey footprints and tracking geologic feature. “Artifacts introduced by sources and receivers during acquisition are automatically removed, substantially relieving the burden on interpreters who used to spend hours meticulously correcting the data,” he notes. “Geologic features are identified automatically, allowing interpreters to navigate through dips, staying on a specific feature even through complex geology.”
These and other automated capabilities save time and help interpreters avoid mental fatigue. “If you spend all your time picking features, there is no time or energy left for analysis,” Al-Yahya observes.
In geostatistical applications, generating and evaluating multiple realizations used to be a processing bottleneck. But processing time has been shortened dramatically by harnessing multiple central processing units, and ranking tools help interpreters sift through hundreds of plausible realizations looking for the most probable, Al-Yahya continues.
“Interpreters focus their energies on adding insight to the process and make adjustments to the initial automated ranking. In this way, technology and interpreter skills are both optimized, leading to improved reservoir characterization,” he concludes.
Software As A Service
Lower computing infrastructure costs enables operators to measure well performance and manage facilities more efficiently, says Oscar Teoh, vice president of operations at iStore. At the same time, easy-to-adopt-and-use devices have become ubiquitous, and users are accustomed to accessing applications using them. This combination of new measures and new access technologies has led to the desire for software as a service (SaaS) applications, he adds. SaaS apps are available over the Internet and simplify the process of distributing access to data.
This new generation of applications fosters collaboration, putting people literally on the same page for tasks ranging from monitoring well performance to forecasting and economics. “Every aspect of operations can be improved with greater access by people in the field and head office,” Teoh says. “Another side of this is the crew change we have been going through,” he adds. “We need to build a wider network of collaboration to keep the expertise available.”
One of the key concepts of SaaS is that it brings the work to people, not the people to work. “When you have this efficiency, the return on investment is high because you do not need a full-time expert. Instead, you have people that you can federate as needed,” explains Teoh.
SaaS also enables users to choose the tool appropriate to them. “Tablets, desktops and collaborative spaces are simply tools that can be used for the right occasion,” he says. “What used to be available on specialized systems is now available on common devices such as smart phones. For example, 3-D images that used to cost millions and require immersive visualization rooms now are available easily through the Internet on affordable platforms that enable users to easily interact with and manipulate subsurface views, such as producing formations and wellbore locations.”
Using software as a service applications, even complex 3-D images are available through the Internet on affordable platforms that let users easily interact with and manipulate subsurface views, such as producing formations and wellbore locations. Shown here is a Web-based 3-D visualization of multilateral wellbores on a seismic horizon structure map.
The best collaborative tools foster and support two-way interaction, where users can touch and move, poke and point, and change data, says Teoh. Optimization in the application enables this interactivity by smartly caching data on the device and selectively transmitting data. Individual workspaces allow users to create and share their own views and edits without affecting the master version.
Standardization and data governance are the underpinnings of effective collaboration. Enforcing rules of ownership and validating data sources are essential to ensuring that the right information is accessed by the right users. Data management is a journey, not a destination, says Teoh. SaaS applications harness the power of the Internet using Web and data services to connect distinct and different databases collected for specific purposes.
“Using web technology, supervisory control and data acquisition data, production data, regulatory reporting data and other data sources can be brought together in a collaborative space for strategic and tactical decision making,” Teoh remarks. “SAAS applications tend to focus on the essentials, avoiding feature overload, proving a more efficient and reliable solution.”
There are two critical factors for efficient HPC seismic processing, according to Charles Sicking, Global Geophysical’s vice president of research and development. The first is turnaround time. In a business where time is literally money, he says operators place a premium on the speed as well as the accuracy of processed results. And that leads to the second factor: quality.
“Quality increases dramatically when clients participate earlier and more often in the processing,” says Sicking. “With faster turnaround times, it becomes reasonable to increase the number of quality reviews. Quality goes sky high when clients get to look at the data in different ways and do more tests over the course of a project.”
Massive parallelization has significantly improved both of these factors, according to Sicking. Parallelization enables simultaneous multinode computations and data access to make processes extremely efficient and save weeks in turnaround time. He says that highly parallelized disk systems enable two kinds of parallelism schemes for seismic processing.
The simplest is course-grain parallelization, whereby each CPU on each node runs the same software application against different parts of the data. In this method, there is no intercommunication between the CPUs, and they do not share memory or compute power. A dataset split across 1,000 CPUs can be processed 1,000 times faster, calculates Sicking.
The second kind is fine-grained parallelism, in which one application runs on a node with multiple CPUs. The application processes one piece of the data using all the CPUs on one node simultaneously. This capability is used extensively for computationally-intensive processes such as reverse-time migration, he notes.
Both kinds of parallelization can be combined by putting a course-grained wrapper around a fine-grained application, Sicking says. Then, for example, a seismic volume containing 50,000 shots can run on 100 nodes with each node processing 500 shots in parallel.
Super highly parallelized disk systems are key to effective parallelization, according to Sicking. Disk storage systems have inherent physical limitations on the speed of data access. “To bypass this limitation, highly parallelized disk systems have many blades with trays holding disks,” he explains. “Each blade has a computer, and all blades communicate and interface with the dataset, which is distributed across hundreds of hard drives. Requests for data are executed in a way that increases disk input/output up to 1,000 times compared with the serial access on single hard drives.”
Data access is fast enough that even datasets with many terabytes can be accessed efficiently, he notes. “When we changed the parallelization of our ambient seismic processing algorithm, the run time went from 2,100 down to 40 equivalent node days on the first large dataset,” Sicking reports. “That huge improvement dramatically shortened turnaround time.”
As another example, Global Geophysical’s seismic imaging application for horizontal transverse isotropy scanning requires very large compute resources, says Sicking. “Our system application uses parallelization to break the computation into small pieces, allowing hundreds of segments to run in parallel. Using this method, many parallel jobs can run simultaneously on hundreds of nodes, allowing for the timely delivery of advanced processing products such as inversion ready gathers,” Sicking says.
The third form of parallelization is to have the entire dataset loaded into memory on many nodes and use all of the CPUs of all nodes to process that dataset. “This method is very useful for transposing multidimensional datasets to change the framework of the data structure. To run effectively, the entire dataset must be accessible simultaneously,” says Sicking. “In a parallelized system, the algorithm shuffles the data until they are completely transposed in memory, and then outputs to the disk system with the new data structure,” he concludes.
Big Data Analytics
“The oil and gas industry is working hard to catch up to the advances in information technology,” says Scott Oelfke, product manager at LMKR, who notes that big data analytics already are being used successfully in financial, manufacturing and retail. One area where Oelfke says he sees some early experimentation with big data technology is in production optimization in unconventional reservoirs.
“With tools such as the open-source Hadoop and SAP’s in-memory HANA platform, the technology exists to leverage big data analytics. If upstream operators can figure out the right questions to ask and what datasets to use, they can get more value from their geological and geophysical data.”
Another area where Oelfke says he sees advancement is managing large data volumes on corporate networks. That is where advanced seismic attribute tools come in, generating high-quality attributes out of huge 3-D volumes, says Oelfke.
“In the past, this process was very time consuming. Today, attributes can be generated using the graphics processing unit and previewed in real time to let interpreters key in on exactly the attribute of interest. The volume can be generated immediately,” he elaborates. “Instead of taking two or three days to generate 12-15 volumes for review, only one volume is created and the process completes in an hour or sooner.”
The processing power in this scenario comes from gaming technology. High-end visualization is cheaper than ever, commoditized by the gaming industry. “Thanks to the power of the GPU, processing and visualizing complex subsurface geology is very fast,” Oelfke states.
To illustrate the sheer volume of data that interpreters must contend with, consider the typical number of wells in a project. “Twenty years ago, 500 wells in a project was a lot of wells, but 500,000 wells are not uncommon today,” says Oelfke. “The scale of these plays is creating huge volumes of data.”
Geosteering is another area benefiting from emerging Web technologies such as HTML5 (the fifth revision of the hypertext markup language standard), and the open-source Angular Web application framework, Oelfke points out. “Moving geosteering to the Web lets operators steer wells anywhere, anytime, 24 hours a day, seven days a week,” he says. “A Web-based tool gives geoscientists the flexibility to get their work done in the office, at home or on the road. It gives these folks their lives back.”
Internet Of Things
Various technologies are converging in ways that result in massive quantities of data being generated in most industries today, but the oil and gas industry has a unique challenge with the types of data being collected as well as the quantity of data, says Felix Balderas, director of technology and product development at Geophysical Insights.
“We need to have the tools to analyze multivariate data because traditional tools were not designed for what is happening with data today,” he remarks. “From upstream to downstream, we are seeing an increased use of data-generating sensors and other devices.”
These devices often are equipped with flash drives, making them more rugged and giving them more storage capability, and faster acquisition and transmission rates, and they are interconnected, Balderas points out.
“This and other increased capacities have produced larger data volumes than we have seen in the past,” he says, adding that the emerging Internet of Things (IOT) opens the possibility for tracking data from all aspects of an operation in real time. “This could provide valuable insights, if the proper tools are available to exploit this information.”
In the seismic acquisition sector, massive volumes of data are generated to create datasets with sizes in terms of terabytes and petabytes, Balderas notes. “These must be analyzed by interpreters, but many of the tools interpreters use were developed when a dataset measured in gigabytes was considered big,” he says. “Fortunately, desktop workstations are keeping pace with performance requirements in most cases, but the challenge continues of how to extract knowledge in a manner that is efficient and effective, given the quantity of data now available.”
Geophysical Insights’ Paradise multiattribute analysis software uses learning machine technology to extract more information from seismic data than is possible using traditional interpretation tools because it learns the data at full seismic resolution
Among the potential solutions are analytical and statistical techniques that cross-correlate apparently disparate data types to find previously unseen relationships that can help optimize dataset selections, such as seismic attributes, and find patterns that reduce the time to identify strategically important geological areas of interest.
“Traditionally, interpreters looked for geological patterns as much visually as numerically, manually picking points to identify geological features. This was a slow and error-prone technique that introduced human bias. The solutions we are developing are based on learning machine (LM) technology,” Balderas says. “Paradise®, the multiattribute analysis software that applies LM technology, extracts more information from seismic data than is possible using traditional interpretation tools because it learns the data at full seismic resolution. And, unlike human interpreters, Paradise is not limited to viewing only two or three attributes at a time.”
What makes LM algorithms different from imperative programming algorithms is that LM can learn from the data, rather than following a set of predefined instructions. Driverless cars, for example, must be able to recognize any stoplight encountered on a route. “There is no way to describe, using instructions, every possible intersection and stoplight configuration,” Balderas explains. “Sooner or later, the car will encounter a stoplight it has not seen before. With LM algorithms, the car will recognize a pattern and adjust what it knows about stoplights for future reference.”
A similar process of pattern recognition and machine learning techniques can shorten the time for extracting knowledge from geophysical data, he contends. “Applied to a volume of geophysical data, the algorithm looks for patterns that reveal geological features, which is essentially what interpreters do,” notes Balderas.
He adds that the speed of pattern recognition is crucial to generating value. “Learning machines can quickly locate faults, horizons and other geological features for the interpreter to review,” Balderas states. “There is no technological substitute for an experienced interpreter, but this ‘candidate feature’ finding approach helps the interpreter focus his work on areas with the greatest potential.”
Seismic attributes, which are any measurable properties of seismic data, aid interpreters in identifying geologic features that are not understood clearly in the original data. However, the enormous amount of information generated from seismic attributes and the difficulty in understanding how these attributes when combined define geology, requires another approach in the interpretation workflow.
To address these issues, “machine learning” to evaluate seismic attributes has evolved over the last few years. Machine learning uses computer algorithms that learn iteratively from the data and adapt independently to produce reliable, repeatable results. Applying current computing technology and visualization techniques, machine learning addresses two significant issues in seismic interpretation:
• The big data problem of trying to interpret dozens, if not hundreds, of volumes of data; and
• The fact that humans cannot understand the relationship of several types of data all at once.
Principal component analysis (PCA) and self-organizing maps (SOMs) are machine learning approaches that when applied to seismic multiattribute analysis are producing results that reveal geologic features not previously identified or easily interpreted. Applying principal component analysis can help interpreters identify seismic attributes that show the most variance in the data for a given geologic setting, which helps determine which attributes to use in a multiattribute analysis using self-organizing maps. SOM analysis enables interpreters to identify the natural organizational patterns in the data from multiple seismic attributes.
Multiple-attribute analyses are beneficial when single attributes are indistinct. These natural patterns or clusters represent geologic information embedded in the data and can help identify geologic features, geobodies, and aspects of geology that often cannot be interpreted by any other means. SOM evaluations have proven to be beneficial in essentially all geologic settings, including unconventional resource plays, moderately compacted onshore regions, and offshore unconsolidated sediments.
This indicates the appropriate seismic attributes to employ in any SOM evaluation should be based on the interpretation problem to be solved and the associated geologic setting. Applying PCA and SOM can not only identify geologic patterns not seen previously in the seismic data, it also can increase or decrease confidence in features already interpreted. In other words, this multiattribute approach provides a methodology to produce a more accurate risk assessment of a geoscientist’s interpretation and may represent the next generation of advanced interpretation.
A seismic attribute can be defined as any measure of the data that helps to visually enhance or quantify features of interpretation interest. There are hundreds of types of attributes, but Table 1 shows a composite list of seismic attributes and associated categories routinely employed in seismic interpretation. Interpreters wrestle continuously with evaluating the numerous seismic attribute volumes, including visually co-blending two or three attributes and even generating attributes from other attributes in an effort to better interpret their data.
This is where machine learning approaches such as PCA and SOM can help interpreters evaluate their data more efficiently, and help them understand the relationships between numerous seismic attributes to produce more accurate results.
Principal Component Analysis
Principal component analysis is a linear mathematical technique for reducing a large set of seismic attributes to a small set that still contains most of the variation in the large set. In other words, PCA is a good approach for identifying the combination of the most meaningful seismic attributes generated from an original volume.
Results from Principal Component Analysis in Paradise® utilizing 18 instantaneous seismic attributes are shown here. 1A shows histograms of the highest eigenvalues for in-lines in the seismic 3-D volume, with red histograms representing eigenvalues over the field. 1B shows the average of eigenvalues over the field (red), with the first principal component in orange and associated seismic attribute contributions to the right. 1C shows the second principal component over the field with the seismic attribute contributions to the right. The top five attributes in 1B were run in SOM A and the top four attributes in 1C were run in SOM B.
The first principal component accounts for as much of the variability in the data as possible, and each succeeding component (orthogonal to each preceding component) accounts for as much of the remaining variability. Given a set of seismic attributes generated from the same original volume, PCA can identify the attributes producing the largest variability in the data, suggesting these combinations of attributes will better identify specific geologic features of interest.
Even though the first principal component represents the largest linear attribute combinations best representing the variability of the bulk of the data, it may not identify specific features of interest. The interpreter should evaluate succeeding principal components also because they may be associated with other important aspects of the data and geologic features not identified with the first principal component.
In other words, PCA is a tool that, when employed in an interpretation workflow, can give direction to meaningful seismic attributes and improve interpretation results. It is logical, therefore, that a PCA evaluation may provide important information on appropriate seismic attributes to take into generating a self-organizing map.
The next level of interpretation requires pattern recognition and classification of the often subtle information embedded in the seismic attributes. Taking advantage of today’s computing technology, visualization techniques and understanding of appropriate parameters, self-organizing maps distill multiple seismic attributes efficiently into classification and probability volumes. SOM is a powerful non- linear cluster analysis and pattern recognition approach that helps interpreters identify patterns in their data that can relate to desired geologic characteristics such as those listed in Table 1.
Seismic data contain huge amounts of data samples and are highly continuous, greatly redundant and significantly noisy. The tremendous amount of samples from numerous seismic attributes exhibit significant organizational structure in the midst of noise. SOM analysis identifies these natural organizational structures in the form of clusters. These clusters reveal significant information about the classification structure of natural groups that is difficult to view any other way. The natural groups and patterns in the data identified by clusters reveal the geology and aspects of the data that are difficult to interpret otherwise.
Offshore Case Study
This shows SOM A results from Paradise on a north-south inline through the field. 1A shows the original stacked amplitude. 2B shows SOM results with the associated five-by-five color map displaying all 25 neurons. 2C shows SOM results with four neurons elected that isolate attenuation effects.
SOM B results from Paradise are shown on the same in-line as Figure 2. 3A is the original stacked amplitude. 3B shows SOM results with the associated five-by-five color map. 3C is the SOM results with a color map showing two neurons that highlight flat spots in the data.
A case study is provided by a lease located in the Gulf of Mexico offshore Louisiana in 470 feet of water. This shallow field (approximately 3,900 feet) has two producing wells that were drilled on the upthrown side of an east-west trending normal fault and into an amplitude anomaly identified on the available 3-D seismic data. The normally pressured reservoir is approximately 100 feet thick and is located in a typical “bright spot” setting, i.e. a Class 3 AVO geologic setting (Rutherford and Williams, 1989).
The goal of this multiattribute analysis is to more clearly identify possible direct hydrocarbon indicator characteristics such as flat spots (hydrocarbon contacts) and attenuation effects and to better understand the reservoir and provide important approaches for decreasing the risk of future exploration in the area.
Initially, 18 instantaneous seismic attributes were generated from the 3-D data in the area. These were put into a PCA evaluation to determine which produced the largest variation in the data and the most meaningful attributes for SOM analysis.
The PCA was computed in a window 20 milliseconds above and 150 milliseconds below the mapped top of the reservoir over the entire survey, which encompassed approximately 10 square miles. Each bar in Figure 1A represents the highest eigenvalue on its associated in-line over the portion of the survey displayed.
An eigenvalue shows how much variance there is in its associated eigenvector, and an eigenvector is a direction showing the spread in the data. The red bars in Figure 1A specifically denote the in-lines that cover the areal extent of the amplitude feature, and the average of their eigenvalue results are displayed in Figures 1B and 1C.
Figure 1B displays the principal components from the selected in-lines over the anomalous feature with the highest eigenvalue (first principal component), indicating the percentage of seismic attributes contributing to this largest variation in the data. In this first principal component, the top seismic attributes include trace envelope, envelope modulated phase, envelope second derivative, sweetness and average energy, all of which account for more than 63 percent of the variance of all the instantaneous attributes in this PCA evaluation.
Figure 1C displays the PCA results, but this time the second highest eigenvalue was selected and produced a different set of seismic attributes. The top seismic attributes from the second principal component include instantaneous frequency, thin bed indicator, acceleration of phase, and dominant frequency, which total almost 70 percent of the variance of the 18 instantaneous seismic attributes analyzed. These results suggest that when applied to a SOM analysis, perhaps the two sets of seismic attributes for the first and second principal components will help define different types of anomalous features or different characteristics of the same feature.
The first SOM analysis (SOM A) incorporates the seismic attributes defined by the PCA with the highest variation in the data, i.e., the five highest percentage contributing attributes in Figure 1B.
Several neuron counts for SOM analyses were run on the data, and lower count matrices revealed broad, discrete features, while the higher counts displayed more detail and less variation. The SOM results from a five-by-five matrix of neurons (25) were selected for this article.
The north-south line through the field in Figures 2 and 3 show the original stacked amplitude data and classification results from the SOM analyses. In Figure 2B, the color map associated with the SOM classification results indicates all 25 neurons are displayed. Figure 2C shows results with four interpreted neurons highlighted.
Based on the location of the hydrocarbons determined from well control, it is interpreted from the SOM results that attenuation in the reservoir is very pronounced. As Figures 2B and 2C reveal, there is apparent absorption banding in the reservoir above the known hydrocarbon contacts defined by the wells in the field. This makes sense because the seismic attributes employed are sensitive to relatively low-frequency, broad variations in the seismic signal often associated with attenuation effects.
This combination of seismic attributes employed in the SOM analysis generates a more pronounced and clearer picture of attenuation in the reservoir than any of the seismic attributes or the original amplitude volume individually. Downdip of the field is another undrilled anomaly that also reveals apparent attenuation effects.
The second SOM evaluation (SOM B) includes the seismic attributes with the highest percentages from the second principal component, based on the PCA (see Figure 1). It is important to note that these attributes are different from the attributes determined from the first principal component. With a five-by-five neuron matrix, Figure 3 shows the classification results from this SOM evaluation on the same north-south line as Figure 2, and it identifies clearly several hydrocarbon contacts in the form of flat spots. These hydrocarbon contacts are confirmed by the well control.
Figure 3B defines three apparent flat spots that are further isolated in Figure 3C, which displays these features with two neurons. The gas/oil contact in the field was very difficult to see in the original seismic data, but is well defined and can be mapped from this SOM analysis.
The oil/water contact in the field is represented by a flat spot that defines the overall base of the hydrocarbon reservoir. Hints of this oil/water contact were interpreted from the original amplitude data, but the second SOM classification provides important information to clearly define the areal extent of reservoir.
Downdip of the field is another apparent flat spot event that is undrilled and is similar to the flat spots identified in the field. Based on SOM evaluations A and B in the field, which reveal similar known attenuation and flat spot results, respectively, there is a high probability this undrilled feature contains hydrocarbons.
West Texas Case Study
Unlike the Gulf of Mexico case study, attribute analyses on the Fasken Ranch in the Permian Basin involved using a “recipe” of seismic attributes, based on their ability to sort out fluid properties, porosity trends and hydrocarbon sensitivities. Rather than use principal component analysis to see which attributes had the greatest variation in the data, targeted use of specific attributes helped solve an issue regarding conventional porosity zones within an unconventional depositional environment in the Spraberry and Wolfcamp formations.
The Fasken Ranch is located in portions of Andrews, Ector, Martin and Midland counties, Tx. The approximately 165,000-acre property, which consists of surface and mineral rights, is held privately. This case study shows the SOM analysis results for one well, the Fasken Oil and Ranch No. 303 FEE BI, which was drilled as a straight hole to a depth of 11,195 feet. The well was drilled through the Spraberry and Wolfcamp formations and encountered a porosity zone from 8,245 to 8,270 feet measured depth.
This enabled the well to produce more than four times the normal cumulative production found in a typical vertical Spraberry well. The problem was being able to find that zone using conventional attribute analysis in the seismic data. Figure 4A depicts cross-line 516, which trends north-south and shows the intersection with well 303. The porosity zone is highlighted with a red circle.
4A is bandwidth extension amplitude volume, highlighting the No. 303 well and porosity zone. Wiggle trace overlay is from amplitude volume. 4B is SOM classification volume, highlighting the No. 303 well and porosity zone. Topology was 10-by-10 neurons with a 30-millisecond window above and below the zone of interest. Wiggle trace overlay is from amplitude volume.
Seven attributes were used in the neural analysis: attenuation, BE14-100 (amplitude volume), average energy, envelope time derivative, density (derived through prestack inversion), spectral decomposition envelop sub-band at 67.3 hertz, and sweetness.
Figure 4B is the same cross-line 516, showing the results of classifying the seven attributes referenced. The red ellipse shows the pattern in the data that best represents the actual porosity zone encountered in the well, but could not be identified readily by conventional attribute analysis.
Figure 5 is a 3-D view of the cluster of neurons that best represent porosity. The ability to isolate specific neurons enables one to more easily visualize specific stratigraphic events in the data.
This SOM classification volume in 3-D view shows the combination of a neural “cluster” that represents the porosity zone seen in the No. 303 well, but not seen in surrounding wells.
Seismic attributes help identify numerous geologic features in conventional seismic data. Applying principal component analysis can help interpreters identify seismic attributes that show the most variance in the data for a given geologic setting, and help them determine which attributes to use in a multiattribute analysis using self-organizing maps. Applying current computing technology, visualization techniques, and understanding of appropriate parameters for SOM enables interpreters to take multiple seismic attributes and identify the natural organizational patterns in the data.
Multiple-attribute analyses are beneficial when single attributes are indistinct. These natural patterns or clusters represent geologic information embedded in the data and can help identify geologic features that often cannot be interpreted by any other means. Applying SOM to bring out geologic features and anomalies of significance may indicate this approach represents the next generation of advanced interpretation.
The authors wish to thank the staff of Geophysical Insights for researching and developing the applications used in this article. The seismic data for the Gulf of Mexico case study is courtesy of Petroleum Geo-Services. Thanks to T. Englehart for insight into the Gulf of Mexico case study. The authors also would like to acknowledge Glenn Winters and Dexter Harmon of Fasken Ranch for the use of the Midland Merge 3-D seismic survey in the West Texas case study.
ROCKY RODEN runs his own consulting company, Rocky Ridge Resources Inc., and works with oil companies around the world on interpretation technical issues, prospect generation, risk analysis evaluations, and reserve/resource calculations. He is a senior consulting geophysicist with Houston-based Geophysical
Insights, helping develop advanced geophysical technology for interpretation.
He also is a principal in the Rose and Associates DHI Risk Analysis Consortium,
which is developing a seismic amplitude risk analysis program and worldwide
prospect database. Roden also has worked with Seismic Microtechnology
and Rock Solid Images on integrating advanced geophysical software applications.
He holds a B.S. in oceanographic technology-geology from Lamar University
and a M.S. in geological and geophysical oceanography from Texas A&M University.
DEBORAH KING SACREY is a geologist/geophysicist with 39 years of oil and gas exploration experience in the Texas and Louisiana Gulf Coast, and Mid-Continent areas. For the past three years, she has been part of a Geophysical Insights team working to bring the power of multiattribute neural analysis of seismic data to the geoscience public. Sacrey received a degree in geology from the University of Oklahoma in 1976, and immediately started working for Gulf Oil. She started her own company, Auburn Energy, in 1990, and built her first geophysical workstation using
Kingdom software in 1995. She specializes in 2-D and 3-D interpretation
for clients in the United States and internationally. Sacrey is a DPA certified
petroleum geologist and DPA certified petroleum geophysicist.
Not long ago, the playbook in unconventional operations called for drilling horizontal wells about anywhere in a “blanket” formation, so long as the wellbore stayed in zone to allow stimulation at regular intervals spaced along the lateral. The name of the game was breaking rock. While well productivity remains a function of creating fractures in low-permeability rock, oil and gas producers have come to appreciate the importance of how and where laterals are placed to ensure access to quality rock.
Advanced Attribute Analysis Techniques that concentrate on a few key seismic attributes have proven highly effective in finding anomalies in subsurface datasets. But Tom Smith, chief executive officer of Geophysical Insights, says technological advances are making it possible to use seismic attributes in almost infinite combinations to delineate anomalies in unconventional plays.
There are hundreds of potential attributes of interest in seismic data, he notes. Reviewing them all to find the best attributes for analysis, and then using them to find sweet spots is a demanding task. “We set out to apply automated, unbiased analysis to this problem,” Smith says. “We developed Paradise™, an advanced geosciences analytic software platform, to enable interpreters to apply these advanced pattern recognition methods to address this problem.”
Smith says Paradise provides workflows that guide geoscientists through the application of unsupervised neural networks (UNNs) and principal component analysis (PCA). “Paradise also takes full advantage of high-power, multicore processing using large-scale parallelism to accelerate the performance of these advanced techniques,” he says.
Unconventional reservoirs have introduced a new suite of rock mechanics properties, and Smith says the industry is still learning which ones provide the most valuable insights. UNNs have the advantage of running uninterrupted and unbiased by human assumptions.
“UNNs offer the advantage of operating on seismic data alone without the need for well logs. Where well logs are available, those data can be included in the UNN analysis, as can data from hydraulic fracturing,” Smith states. “The more data provided to the system, the more information we can discern from the results. We view this process as advantageous because we make no assumptions about linear or statistical combinations.”
Smith points out that even the most detailed well logs represent a tiny sampling of the subsurface. “It is not that obvious how to sort the properties,” he observes. “Running a supervised neural network is problematic in unconventional plays because the rock properties known at the borehole are an extremely limited sample set. Better tools are needed to lower exploration risk in unconventional plays. By applying both UNN and PCA on the seismic response, greater insights can be realized about the geology and sweet spots identified.”
UNNs look at the natural properties and find natural clusters that are not artificially biased in any way. “We are working in n-dimensional space, where n is the number of attributes,” Smith details. “Attributes can vary in data type and some parameters are predetermined.”
In unconventional formations, interpreters typically search for overpressured zones, sweet spots, AVO and fracture networks. They also look for anomalies and anything that is out of the ordinary. “Neural networks can scan large volumes to find areas of interest for further analysis,” Smith explains. “This capability enables interpreters to focus more effectively and efficiently.”
While the results of attribute analysis are presented in a 3-D cube, Smith says his team has built a 2-D color bar in the Paradise software to more effectively analyze and interact with the volume. The user selects a few neurons on the 2-D color bar, and the 3-D representation highlights only the regions in the volume that correspond to those neurons, enabling isolation of the classification results.
Geophysical Insights’ Paradise™ analytic software platform applies automated pattern recognition to analyze seismic attributes in almost infinite combinations to delineate anomalies in unconventional plays. The workflows use unsupervised neural networks and principal component analysis while taking advantage of high-power multicore processing using large-scale parallelism to accelerate performance. Shown here are attribute analysis results presented in a 3-D viewer with a 2-D color bar for interacting with the data volume.