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Abstract Multi-stage, multi-well completions cause pore-pressures to increase around each stage treated, compound from earlier offset treatment stages, then dissipate as the injected fluid leaks off into the rock formation. Rock stresses change in a dynamic fashion from virgin reservoir stress to an altered stress influencing subsequently treated stages which can restrict slurry propagation from these injections into regions experiencing excess stress. Stress shadows are time-dependent and dissipate over time and return to the virgin stress state. Microseismic focal mechanisms detected from a high-fold wide azimuth surface array can be used to observe and calculate stress changes in the reservoir and constrain the time it takes for stresses to return to the virgin reservoir state. Operators can take advantage of stress changes and contain fractures close to the stages by building stress wedges around subsequently treated stages. After stress dissipates fluid propagates into previously opened fractures leading to poor fracture containment. In this paper, we review the effects of time-dependent stress shadows on multi-well completions in the Wolfcamp Formation in Southeast New Mexico. Then radioactive tracer data from the Niobrara Formation in the Denver-Julsburg basin is analyzed to provide further verification of the time-dependent process. Increased stresses from previous treatments remain elevated for ∼7 days which push fluid injected on neighboring wells away from the stress shadow. Production of well-specific tracer corroborates the hypothesis that local stress-shadows are elevated for ∼7 days which can push fluid from subsequent neighboring wells. After stresses dissipate through the fractures created during the initial stimulation, new tracer on offset wells was produced as much as 3,000 ft away on a neighboring well. Introduction Microseismic monitoring is a proven technology for observing and mapping reservoir response to hydraulic fracture stimulations. The event radiation pattern of the P-wave first arrival reveals advanced characteristics of the fracture describing deformation at the source location when detected using a high-fold wide azimuth surface array. The full-moment tensor can be generally decomposed into the relative percentages of isotropic, double couple and compensated linear vector dipole components (e.g. Aki and Richards, 1980) which fully describes the failure process in terms of volume change, amount of shearing, and other complexities related to deformation. The local stress field can be calculated using a set of focal mechanisms by minimizing the misfit angle between the modeled stress field and the observed focal mechanism slip vectors (Angelier, 1989) where the local stress field extent is defined by the spatial extent of the observed focal mechanisms. The local stress field orientation and relative magnitude can be resolved for a group of microseismic focal mechanisms by minimizing the misfit angle between the modeled stress field and the observed focal mechanism slip vectors for the subsets using a method described by Vavrycuk, 2014.
- North America > United States > New Mexico (0.55)
- North America > United States > Texas (0.35)
- North America > United States > Wyoming (0.34)
- (3 more...)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Wyoming > DJ (Denver-Julesburg) Basin > Niobrara Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (7 more...)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Tracer test analysis (1.00)
On the Path to Least Principal Stress Prediction: Quantifying the Impact of Borehole Logs on the Prediction Model
Dvory, N. Z. (Civil & Environmental Engineering & Energy and Geoscience Institute) | Smith, P. J. (Chemical Engineering) | McCormack, K. L. (Energy and Geoscience Institute) | Esser, R. (Energy and Geoscience Institute) | McPherson, B. J. (Civil & Environmental Engineering & Energy and Geoscience Institute)
ABSTRACT Knowledge of the minimum horizontal principal stress (Shmin) is essential for geo-energy utilization. Shmin direct measurements are costly, involve high-risk operations, and provide only discrete values of the required quantity. Other methods were developed to interpret a continuous stress sequence from sonic logs. These methods usually require some ‘horizontal tectonic stress’ correction for calibration and rarely match sections characterized by stress profiling due to viscoelastic stress relaxation. Recently, several studies have tried to predict the stress profile by an empirical correlation corresponding to an average strain rate through geologic time or by using machine learning technologies. Here, we used the Bayesian Physics-Based Machine Learning framework to identify the relationships among the viscoelastic parameter distributions and to quantify statistical uncertainty. More specifically, we used well logs data and ISIP measurements to quantify the uncertainty of the viscoelastic-dependent stress profile model. Our results show that the linear regression approach suffers from higher uncertainty, and the Gaussian process regression Shmin prediction shows a relatively smaller uncertainty distribution. Extracting the lithology logs from the prediction model improves each method's uncertainty distribution. We show that the density and the porosity logs have a superior correlation to the viscoplastic stress relaxation behavior. INTRODUCTION Comprehensive recognition of the least principal stress is essential for economic multistage hydraulic fracturing stimulation design. It is well established that hydraulic fractures propagate perpendicular to the least principal stress and that the stress profile prominent the hydraulic fractures generation in both the lateral and horizontal direction (Fisher et al., 2012; Hubbert and Willis, 1957; Kohli et al., 2022; Valkó and Economides, 1995; Zoback et al., 2022)c. In other words, the stress layering could act as a ‘frac barrier’ that limits fracture development in discrete directions and promotes progress in different directions (Singh et al., 2019). Detailed knowledge of the least principal stress profile is significant for hydraulic fracture growth assessment, proppants technology optimization, and efficient landing zone detection (Pudugramam et al., 2022). Traditionally, these considerations were aligned with the oil and gas industry. Still, today, they have substantial implications for enhanced geothermal system development, carbon storage integrity, and in a broader sense, a safe path for a carbon neutrality economy.
- Geology > Rock Type (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- North America > United States > Wyoming > DJ (Denver-Julesburg) Basin > Niobrara Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- North America > United States > Texas > Permian Basin > Midland Basin (0.99)
- (13 more...)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
High-Resolution Core Study Relating Chemofacies to Reservoir Quality: Examples from the Permian Wolfcamp XY Formation, Delaware Basin, Texas
Putri, Shaskia Herida (Colorado School of Mines) | Jobe, Zane (Colorado School of Mines) | Wood, Lesli J. (Colorado School of Mines) | Melick, Jesse (Colorado School of Mines) | French, Marsha (Colorado School of Mines) | Pfaff, Katharina (Colorado School of Mines)
Abstract The Wolfcamp and Bone Spring Formations are comprised of siliciclastic and carbonate sediment gravity flow deposits, including turbidites and debrites that were sourced from multiple uplifted areas and deposited in the Delaware Basin, Texas during the early-middle Permian (Early Leonardian, ∼285 Ma). Deep-water lobe deposits in these formations are primary unconventional reservoir targets in the North-central Delaware Basin of Texas. Despite numerous recent reservoir characterization studies in this area, integrated multi-scale core-based studies relating to reservoir quality are sparsely published. This research aims to provide a workflow to better predict source rock and reservoir distribution by integrating geochemistry and petrophysical data from this deep-water depositional system. Using high-resolution (1 cm), continuous X-ray fluorescence (XRF) data from 218 feet of core from the Wolfcamp XY interval, this study focuses on the controls that depositional processes and diagenesis impart on chemofacies. Unsupervised k-means clustering and principal component analysis on 17 XRF-derived elemental concentrations derive four chemofacies that characterize geochemical heterogeneity: (1) calcareous, (2) oxic-suboxic argillaceous, (3) anoxic argillaceous, and (4) detrital mudrock. Results indicate that vertical, event-bed-scale variations in XRF-based chemofacies accurately represent depositional facies changes, often matching cm-by-cm the human-described lithofacies. This research demonstrates the relationship of chemofacies to petrophysical properties (e.g., total organic carbon, porosity, and water saturation), which can be used for log-based reservoir prediction of the Wolfcamp and Bone Spring Formations in the Permian Basin, as well as for other mixed clastic-carbonate deep-water reservoirs around the world. Introduction Mixed siliciclastic-carbonate mudstone unconventional reservoirs contain complex sub-well-log-scale heterogeneity in mineralogical composition due to depositional process variability (Lazar et al., 2015; Comerio et al., 2020, Kvale et al., 2020; Ochoa et al., 2022). Moreover, these lithofacies are organized as repetitive meter-scale sedimentation units that are linked to depositional-element architectural and sequence stratigraphic evolution (Thompson et al., 2018; Zhang et al., 2021). High-resolution core studies can help to capture fine-scale depositional units and diagenetic process (Baumgardner et al., 2014; Ochoa et al., 2022). Because it is difficult to visually observe the heterogeneity in mixed siliciclastic-carbonate mudstone cores, it is crucial to integrate quantitative petrophysical analyses with mineralogical and geochemical data to improve the accuracy of predictive models (Lazar et al., 2015; Ochoa et al., 2022).
- North America > United States > Texas (1.00)
- North America > United States > New Mexico (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geophysics > Seismic Surveying (0.93)
- Geophysics > Borehole Geophysics (0.88)
- South America > Argentina > Patagonia > Neuquén > Neuquen Basin (0.99)
- North America > United States > Wyoming > Powder River Basin (0.99)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- (55 more...)
Oil and Gas Generation, Migration, Production Prediction, and Reservoir Characterization of Northern Denver Basin: Implication from the Total Petroleum Systems
Rahman, Mohammad 'Wahid' (Impac Exploration Services, Inc. / Ossidiana Energy) | Fox, Mathew (Ossidiana Energy) | Kramer, Darrell (Ossidiana Energy) | Mullen, Chris (Ossidiana Energy)
Abstract The Denver-Julesburg (Dj) basin has multiple oils and gas producing unconventional reservoirs but the oil-source-reservoir correlation of hydrocarbon from these reservoirs are not well fingerprinted through detailed geochemistry dataset. It is important to determine the origin of hydrocarbons to estimate the hydrocarbon phase, GOR and production prediction. Many of these reservoir parameters vary based on the type of source rock and nature of its expulsion in varying PVT conditions. This study focuses on the detailed geochemistry from source rock, extracted oil, mud gas, and production gas and oil to determine the origin of the hydrocarbon stored in different Cretaceous intervals from Denver basin and their production equivalent phases. Geochemistry data were generated from cored rocks, cuttings, mud gas, extracted oils and compared with the produced gas and oils from the Denver basin. This article includes source rock analysis through Rock-Eval pyrolysis on cored and cuttings rocks, Leco-TOC, gas composition and compound specific isotopes via GC-IRMS, thermal extract gas chromatography (TEGC), high resolution gas chromatography, Gas Chromatography-Mass Spectrometry (GCMS) biomarker analysis on MPLC (medium pressure liquid chromatography) separated saturates and aromatics, bulk carbon isotope analysis on extracted oil and produced oil (Peters et al., 2005; Rahman et al., 2016; Rahman et al., 2017). Clayton and Swetland (1980) concluded that all the Cretaceous oils are compositionally similar throughout the basin. But the extracted oils from cored rock and cuttings and associated gas and oil data from several intervals from this study clearly depict there are significant differences in oils found in these Cretaceous reservoirs. Geochemistry data from source rock suggests that most of the organic matter in different Cretaceous source rocks are of Type II kerogen. However, the source rock differs in chemistry because of depositional environment associated with marine shale vs carbonate. It is evident from the pyrolysis, mud gas, and extracted oil chemistry data from the Denver basin that there are distinct differences in origin of oil and gas in these reservoirs. The major highpoints of this study are as follows: 1) the novel organic geochemistry data should be used to better characterize any basin for conventional and unconventional exploration and development; 2) this approach helps to model better petroleum systems, basin evaluation, and overall understanding of the quality of petroleum, expulsion histories, migration pathways and type of petroleum stored in rocks.
- North America > United States > Wyoming (1.00)
- North America > United States > Nebraska (1.00)
- North America > United States > Kansas (1.00)
- North America > United States > Colorado (1.00)
- Geology > Geological Subdiscipline > Geochemistry (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.72)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.36)
- North America > United States > Wyoming > Powder River Basin (0.99)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Wyoming > DJ (Denver-Julesburg) Basin > Niobrara Formation (0.99)
- (47 more...)
Summary In this study, we developed a data mining-based multivariate analysis (MVA) workflow to identify correlations in complex high-dimensional data sets of small size. The research was motivated by the integration analysis of geologic, geophysical, completion, and production data from a 4-square-mile study field located in the Northern Denver-Julesburg (DJ) Basin, Colorado, USA. The goal is to establish a workflow that can extract learnings from a small data set to guide the future development of surrounding acreages. In this research, we propose an MVA workflow, which is modified significantly based on the random forest algorithm and assessed using the R score from K-fold cross-validation (CV). The MVA workflow performs significantly better in small data sets compared to traditional feature selection methods. This is because the MVA workflow includes (1) the selection of top-performing feature combinations at each step, (2) iterations embedded, (3) avoidance of random correlation, and (4) the summarization of each feature’s occurrence at the end. When the MVA workflow was initially applied on a complex synthetic small data set that included numerical and categorical variables, linear and nonlinear relationships, relationships within independent variables, and high dimensionality, it correctly identified all correlating variables and outperformed traditional feature selection methods. Following that, a field data set consisting of the information from 23 wells was investigated using the MVA workflow aiming at identifying the key factors that affect the production performance in the study area. The MVA workflow reveals the weak correlation between production and legacy well effect. The results show that the key factors affecting production in this study area are total organic carbon (TOC) percentage, open fracture densities, clay content, and legacy well effect, which should receive significant attention when developing neighboring acreage of the DJ Basin. More importantly, this MVA method can be implemented in other basins. Considering the heterogeneity of unconventional resources, it is worthwhile to identify the key production drivers on a small scale. The outperformance of this MVA method on small data sets makes it possible to provide valuable insights for each specific acreage.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (0.88)
Discrete Measurements of the Least Horizontal Principal Stress from Core Data: An Application of Viscoelastic Stress Relaxation
McCormack, Kevin L. (Energy and Geoscience Institute, University of Utah) | McLennan, John D. (Energy and Geoscience Institute, University of Utah) | Jagniecki, Elliot A. (Utah Geological Survey) | McPherson, Brian J. (Energy and Geoscience Institute, University of Utah (Corresponding author))
Summary The emerging Paradox Oil Play in southeastern Utah is among the most significant unconventional plays in the western USA. The mean total undiscovered oil resources within just the Pennsylvanian Cane Creek interval of the Paradox Basin are believed to exceed 215 million barrels. However, to date, less than 5% (~9 million barrels) of the total Cane Creek resource has been produced from fewer than 40 wells, and only approximately one-half of those are horizontal wells. More than 95% of production is from the central Cane Creek Unit (CCU). Natural fractures are a key feature of many production wells, but stimulation by induced hydraulic fractures is not consistently successful. We hypothesize that more effective production in this play will rely on better fundamental characterization, especially on better quantification of the state of stress. Approximately 110 ft of core, well logs, and a diagnostic fracture injection test (DFIT) were acquired from the State 16-2 well within the CCU. With these data, we applied two methods to constrain and clarify the state of stress. The first technique, the Simpson’s coefficient method, provides lower bounds on the two horizontal principal stresses and relies on only limited data. Alternatively, the viscoelastic stress relaxation (VSR) method is used to estimate the least horizontal principal stress, building on observations that principal stresses become more isotropic as the viscous behavior of a rock is more pronounced. Results of these two methods support the hypothesis that the state of stress in the CCU of the Paradox Basin is nearly lithostatic and isotropic. Other factors consistent with this hypothesis include high formation pore pressure, which tends to reduce the possible stress states by changing the frictional failure equilibrium; lack of induced fractures in the core, which should be present in the case of stress anisotropy; and interbedded halite layers, which given their high degree of ductility, probably lead to greater VSR for the entire sedimentary package.
- North America > United States > Utah (1.00)
- North America > United States > Colorado (1.00)
- Phanerozoic > Paleozoic > Carboniferous > Pennsylvanian (1.00)
- Phanerozoic > Mesozoic (0.93)
- Geology > Mineral (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Structural Geology > Tectonics > Plate Tectonics (0.93)
- (3 more...)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (0.66)
- North America > United States > Wyoming > DJ (Denver-Julesburg) Basin > Niobrara Formation (0.99)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- (23 more...)
Summary Evaluating hydraulic fracturing completion is critical for low porosity, low permeability unconventional reservoir development. In this study, we use low-frequency distributed acoustic sensing (LF-DAS) measurements to monitor the hydraulic fracturing in the Chalk Bluff field in the Denver-Julesburg (DJ) Basin, Colorado. Interpreting fracture-hits from crosswell LF-DAS data yields insights into the fracture geometry and propagation across and within two targeted formations: Niobrara and Codell. We observe significant differences in hydraulic fracture propagation between the two formations; the half length of hydraulic fractures in the Codell formation is much longer than that in the Niobrara. In addition, hydraulic fracture propagates significantly faster in Codell than in Niobrara under the same pumping rate. The differences could be explained by higher natural fracture density and potentially lower stress anisotropy in the Niobrara formation. We also observed different fracture orientations between the two formations and inconsistent fracture orientations within Niobrara. Hydraulic fractures observed in Codell oriented at 100 degree consistently, while two group of fracture azimuths (110 and 240 degrees) can be observed in Niobrara. The difference in fracture orientations in Niobrara and Codell indicates stress regime changes between the formations. The inconsistency of fracture azimuth in Niobrara may be caused by the zipper fracturing sequence. Strong cross-formation fracture connections between the two formations can also be observed, with different up-going and down-going fracture propagation velocities. These observations help us better understand the complex fracture geometry in the DJ Basin and provide critical constraints on the optimization of the unconventional reservoir development.
- North America > United States > Wyoming (1.00)
- North America > United States > Colorado (1.00)
- North America > United States > Wyoming > DJ (Denver-Julesburg) Basin > Niobrara Formation (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- (33 more...)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Summary In this study, we developed a data mining-based multivariate analysis (MVA) workflow to identify correlations in complex high-dimensional data sets of small size. The research was motivated by the integration analysis of geologic, geophysical, completion, and production data from a 4-square-mile study field located in the Northern Denver-Julesburg (DJ) Basin, Colorado, USA. The goal is to establish a workflow that can extract learnings from a small data set to guide the future development of surrounding acreages. In this research, we propose an MVA workflow, which is modified significantly based on the random forest algorithm and assessed using the R score from K-fold cross-validation (CV). The MVA workflow performs significantly better in small data sets compared to traditional feature selection methods. This is because the MVA workflow includes (1) the selection of top-performing feature combinations at each step, (2) iterations embedded, (3) avoidance of random correlation, and (4) the summarization of each feature’s occurrence at the end. When the MVA workflow was initially applied on a complex synthetic small data set that included numerical and categorical variables, linear and nonlinear relationships, relationships within independent variables, and high dimensionality, it correctly identified all correlating variables and outperformed traditional feature selection methods. Following that, a field data set consisting of the information from 23 wells was investigated using the MVA workflow aiming at identifying the key factors that affect the production performance in the study area. The MVA workflow reveals the weak correlation between production and legacy well effect. The results show that the key factors affecting production in this study area are total organic carbon (TOC) percentage, open fracture densities, clay content, and legacy well effect, which should receive significant attention when developing neighboring acreage of the DJ Basin. More importantly, this MVA method can be implemented in other basins. Considering the heterogeneity of unconventional resources, it is worthwhile to identify the key production drivers on a small scale. The outperformance of this MVA method on small data sets makes it possible to provide valuable insights for each specific acreage.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (0.88)
ABSTRACT: In this study, a special focus was dedicated to the effect of elastic anisotropy of shales on the in-situ stress contrast between different layers and its implications on the vertical containment of hydraulic fractures (HF) and how they relate to the widely observed fracture driven interaction (FDI) phenomena and undesirable HF height growth. The reported elastic and mechanical properties of the main members of the Bakken petroleum system in the Williston Basin (i.e. Upper and Lower Bakken Shale, Middle Bakken, and Three Forks) were used to estimate the in-situ stresses based on anisotropic rock properties and use the minimum horizontal stress profile for HF modeling. The estimated stress profile appeared to be very different from the one calculated based on the isotropic formation assumption. The anisotropic stress model, as reported by other researchers, is more realistic in transversely isotropic rocks and rocks with a high volume of clay and TOC and generated more reliable results that conform better with other indicators and observations from other types of data associated with HF geometry. 1. INTRODUCTION Accurate estimation of the minimum principal in-situ stress is a milestone in the successful design of hydraulic fracturing (HF) jobs (Ganpule et al., 2015). The role of accurate stress variations with depth becomes more pronounced where HF is performed in different horizons to explore what is called stacked pay. Estimation of in-situ horizontal stresses is mainly attributed to the inherently simplistic assumptions of the commonly used stress models (Zoback, 2007). However, the revolution in the oil and gas industry due to production from Shale plays indicated the necessity of using anisotropic, or what is so-called as transverse isotropic (TI) assumption for the different geological layers for improved estimation of the horizontal stresses. In this study, we showcased the importance of laboratory characterization of the elastic and mechanical properties for accurate prediction of stress profiles and how they can change our designs and improve the profitability of our investments by generating more reliable stress models that are coherent with what is indicated by other types of data. This can serve as a strong base for improved planning. This is proved through our case study performed using data from the Bakken petroleum system in the Williston Basin. It was found that the HF geometries predicted from simulation using a well-calibrated anisotropic stress model strongly agreed with HF geometries observations from microseismic data reported in many other studies such as McKimmy et al. (2022) and Lorwongngam et al. (2018).
- North America > United States > South Dakota (1.00)
- North America > United States > North Dakota (1.00)
- North America > United States > Montana (1.00)
- (2 more...)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.70)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.59)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play > Shale Oil Play (0.37)
- North America > United States > Wyoming > DJ (Denver-Julesburg) Basin > Niobrara Formation (0.99)
- North America > United States > South Dakota > Williston Basin > Bakken Shale Formation (0.99)
- North America > United States > North Dakota > Williston Basin > Three Forks Group Formation (0.99)
- (12 more...)
Abstract Objectives/Scope This study will demonstrate an automated machine learning approach for fault detection in a 3D seismic volume. The result combines Deep Learning Convolution Neural Networks (CNN) with a conventional data pre-processing step and an image processing-based post processing approach to produce high quality fault attribute volumes of fault probability, fault dip magnitude and fault dip azimuth. These volumes are then combined with instantaneous attributes in an unsupervised machine learning classification, allowing the isolation of both structural and stratigraphic features into a single 3D volume. The workflow is illustrated on a 3D seismic volume from the Denver Julesburg Basin and a statistical analysis is used to calibrate results to well data. Methods/Procedures/Process Starting with a seismic amplitude volume, the method has four steps. Pre-processing produces the volume used as input to the CNN fault classification and the dip volumes used in post processing. Next, CNN applies a 3D synthetic fault engine to predict faults. Then, a directional 3D Laplacian of Gaussian filter enhances the faults in their primary direction and the final step, skeletonization, produces skeletonized probability, dip and azimuth. The result is higher quality when compared to the output from CNN alone (without pre and post processing). The fault volumes are next combined with instantaneous attributes in an unsupervised machine learning classification through Self-Organizing Maps (SOMs) to produce a classification volume from which faults and reservoir neurons can be isolated, calibrated to wells and converted to multi-attribute geobodies. Results/Observations/Conclusions The results provide a rapid, robust, and unbiased fault interpretation which can be used to create either fault plane or fault stick interpretations in a standard interpretation package. The SOM is preceded by principal component analysis to identify prominent attributes. These resolve the seismic character of the analysis interval (Top Niobrara to Top Greenhorn). In addition to enhanced fault identification, the Niobrara's brittle chalk benches are easily distinguished from more ductile shale units and individual benches; A, B, and C benches each have unique sets of characteristics to isolate them in the volume. Extractions from SOM volumes at wells confirm the statistical relationships between SOM neurons and reservoir properties. Applications/Significance/Novelty Traditional seismic interpretation, including fault interpretation and stratigraphic horizon picking, is poorly suited to the demands of unconventional drilling with its typically high well densities. Geophysicists devote much of their efforts to well planning and working with the drilling team to land wells. Machine learning applied in seismic interpretation offers significant benefits by automating tedious and somewhat routine tasks such as fault and reservoir interpretation. Automation reduces the fault interpretation time from weeks/days to days/hours. Multi-attribute analysis accelerates the process of high grading reservoir sweet spots with the 3d volume. Statistical measures make the task of calibrating the unsupervised results feasible.
- North America > United States > Wyoming (1.00)
- North America > United States > Colorado (1.00)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.35)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Wyoming > DJ (Denver-Julesburg) Basin > Niobrara Formation (0.99)
- North America > United States > Nebraska > Laramie Basin > Niobrara Formation (0.99)
- (9 more...)