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Collaborating Authors
North America
A One-Dimensional Convolutional Neural Network for Fast Predictions of the Oil-CO2 Minimum Miscibility Pressure in Unconventional Reservoirs
Sun, Hao (Schulich School of Engineering, University of Calgary) | Chen, Zhangxin (Schulich School of Engineering, University of Calgary (Corresponding author))
Summary Miscible carbon dioxide (CO2) injection has proven to be an effective method of recovering oil from unconventional reservoirs. An accurate and efficient procedure to calculate the oil-CO2 minimum miscibility pressure (MMP) is a crucial subroutine in the successful design of a miscible CO2 injection. However, current numerical methods for the unconventional MMP prediction are very demanding in terms of time and computational costs which result in long runtime with a reservoir simulator. This work proposes to employ a one-dimensional convolutional neural network (1D CNN) to accelerate the unconventional MMP determination process. Over 1,200 unconventional MMP data points are generated using the multiple-mixing-cell (MMC) method coupled with capillarity and confinement effects for training purposes. The data set is first standardized and then processed with principal component analysis (PCA) to avoid overfitting. The performance of the proposed model is evaluated with testing data. By applying the trained model, the unconventional MMP results are almost instantly produced and a coefficient of determination of 0.9862 is achieved with the testing data. Notably, 98.58% of predicting data points lie within 5% absolute relative error. This work demonstrates that the prediction of unconventional MMP can be significantly accelerated, compared with the numerical simulations, by the proposed well-trained deep learning model with a slight impact on the accuracy.
- North America > Canada > Alberta (0.46)
- Asia > Middle East > UAE (0.28)
- North America > United States > Texas (0.28)
- North America > United States > South Dakota > Williston Basin > Bakken Shale Formation (0.99)
- North America > United States > North Dakota > Williston Basin > Bakken Shale Formation (0.99)
- North America > United States > Montana > Williston Basin > Bakken Shale Formation (0.99)
- (7 more...)
A New Method to Reduce Shale Barrier Effect on SAGD Process: Experimental and Numerical Simulation Studies using Laboratory-Scale Model
Dong, Xiaohu (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing) (Corresponding author)) | Liu, Huiqing (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Tian, Yunfei (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Liu, Siyi (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Li, Jiaxin (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Jiang, Liangliang (Department of Chemical and Petroleum Engineering, University of Calgary) | Chen, Zhangxin (Department of Chemical and Petroleum Engineering, University of Calgary)
Summary Shale barrier has been widely reported in many steam-assisted gravity drainage (SAGD) projects. For an SAGD project, the properties and distribution of shale barrier can significantly impede the vertical expansion and lateral spread of steam chamber. Currently, although some literature has discussed the shale barrier effect from different perspectives, a systematic investigation combining the scaled physical and numerical simulations is still lacking. Simultaneously, how to reduce the shale barrier effect is also challenging. In this study, aiming at the Long Lake oilsands resources, combining the methods of 3D experiment and numerical simulation, a new method based on a top horizontal injection well is proposed to reduce the impact of shale barrier on the SAGD process. First, based on a dimensionless scaling criterion of gravity-drainage process, we conducted two 3D gravity-drainage experiments (base case and improved case) to explore the effect of shale barrier and the performance of top injection well on SAGD production. During experiments, to improve the similarity between the laboratory 3D model and the field prototype, a new wellbore model and a physical simulation method of shale barrier are proposed. The location of the shale barrier is placed above the steam injection well, and the top injection well is set above the shale barrier. For an improved case, once the steam chamber front reaches the horizontal edge of the shale barrier, the top injection well can be activated as a steam injection well to replace the previous steam injection well in the SAGD well pair. From the experimental observation, the effect of the top injection well is evaluated. Subsequently, a set of numerical simulation runs are performed to match the experimental measurements. Therefore, from this laboratory-scale simulation model, the effect of shale barrier size is discussed, and the switch time of the top injection well is also optimized to maximize the recovery process. Experimental results indicate that a top injection well-based oil drainage mode can effectively unlock the heavy crude oil above shale barrier and improve the entire SAGD production. Compared with a basic SAGD case, the top injection well can increase the final oil recovery factor by about 8%. Simultaneously, through a mass conservation law, it is calculated that the unlocking angle of remaining oil reserve above the shale barrier is about 6ยฐ. The angle can be used to effectively evaluate the recoverable oil reserve after the SAGD process for the heavy oil reservoir with a shale barrier. The simulation results of our laboratory-scale numerical simulation model are in good agreement with the experimental observation. The optimized switch time of the top injection well is the end of the second lateral expansion stage. This paper proposes a new oil drainage mode that can effectively reduce the shale barrier effect on SAGD production and thus improve the recovery performance of heavy oil reservoirs.
- Asia > Middle East > UAE (0.46)
- Asia > China (0.28)
- North America > Canada > Alberta (0.28)
- Overview > Innovation (0.60)
- Research Report > New Finding (0.49)
- North America > Canada > Alberta > Athabasca Oil Sands > Western Canada Sedimentary Basin > Alberta Basin > MacKay River Oil Sands Project (0.99)
- North America > Canada > Alberta > Athabasca Oil Sands > Western Canada Sedimentary Basin > Alberta Basin > Long Lake Oil Sands Project (0.97)
A Physics-Informed Spatial-Temporal Neural Network for Reservoir Simulation and Uncertainty Quantification
Bi, Jianfei (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing) / Department of Chemical and Petroleum Engineering, University of Calgary) | Li, Jing (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing) (Corresponding author)) | Wu, Keliu (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Chen, Zhangxin (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing) / Department of Chemical and Petroleum Engineering, University of Calgary (Corresponding author)) | Chen, Shengnan (Department of Chemical and Petroleum Engineering, University of Calgary) | Jiang, Liangliang (Department of Chemical and Petroleum Engineering, University of Calgary) | Feng, Dong (National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Deng, Peng (Department of Chemical and Petroleum Engineering, University of Calgary)
Summary Surrogate models play a vital role in reducing computational complexity and time burden for reservoir simulations. However, traditional surrogate models suffer from limitations in autonomous temporal information learning and restrictions in generalization potential, which is due to a lack of integration with physical knowledge. In response to these challenges, a physics-informed spatial-temporal neural network (PI-STNN) is proposed in this work, which incorporates flow theory into the loss function and uniquely integrates a deep convolutional encoder-decoder (DCED) with a convolutional long short-term memory (ConvLSTM) network. To demonstrate the robustness and generalization capabilities of the PI-STNN model, its performance was compared against both a purely data-driven model with the same neural network architecture and the renowned Fourier neural operator (FNO) in a comprehensive analysis. Besides, by adopting a transfer learning strategy, the trained PI-STNN model was adapted to the fractured flow fields to investigate the impact of natural fractures on its prediction accuracy. The results indicate that the PI-STNN not only excels in comparison with the purely data-driven model but also demonstrates a competitive edge over the FNO in reservoir simulation. Especially in strongly heterogeneous flow fields with fractures, the PI-STNN can still maintain high prediction accuracy. Building on this prediction accuracy, the PI-STNN model further offers a distinct advantage in efficiently performing uncertainty quantification, enabling rapid and comprehensive analysis of investment decisions in oil and gas development.
- North America > United States (1.00)
- North America > Canada > Alberta (0.28)
Abstract Currently, the most effective way to extract resources from shale reservoirs is through the use of multi-stage fracturing of horizontal wells. However, the process of flowback after fracturing can affect the extent of damage done by the fracturing fluid to the formation and fracture conductivity, which ultimately impacts the success of the fracturing process. Unfortunately, the control of flowback in fracturing fluid relies on empirical methods, and lacks a reliable theoretical foundation. As a result, it is important to optimize the flowback process by controlling the velocity and flowback of the fracturing fluid. Additionally, previous research on the productivity of multi-stage fracturing horizontal wells after fracturing is limited, and the equation derivation process has been oversimplified, leading to reduced accuracy. This paper introduced a new model to adjust a choke size to optimize flowback velocity and predicts production performance following fracturing. To enhance fracture clean-up efficiency, choke sizes are dynamically adjusted based on wellhead pressure changes over time. A two-phase flow model is used, and factors like proppant particle forces, filtration loss, fluid compressibility, wellbore friction, and gas slippage are taken into account. Using mass conservation theory, the model predicts production performance for multi-fractured horizontal wells, considering dual-porosity, stress-sensitivity, gas adsorption and desorption, and gas and water relative permeabilities. The above model was used to evaluate a multi-fractured horizontal well (MFHW) based on shale reservoir parameters in order to study the factors affecting flowback and production after fracturing. The study examined the relationship between proppant particle diameter, choke size, and wellhead pressure, as well as the impact of different choke sizes on gas production after flowback. The findings showed that as proppant particle diameter increased, choke size also increased. However, if the choke size is too large or too small during the flowback process, it will reduce fracture conductivity. The optimal choke size was found to be between 4-6 mm and dynamic adjustment of choke size based on changes in wellhead pressure resulted in the best fracture conductivity and highest productivity for a horizontal well.
- North America > United States > Texas (1.00)
- Asia (0.68)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- Asia > China > Sichuan > Sichuan Basin (0.99)
Abstract The development of a geothermal system can supply low-carbon electricity to support the raising energy demand under the energy transition from fossil fuel to renewables. CO2 can substitute for water for energy recovery from geothermal reservoirs owing to its better mobility and higher heat capacity. Additionally, trapping injected CO2 underground can achieve environmental benefits by targeting Greenhouse gas (GHG) mitigation. In this study, different flow schemes are established to assess heat mining and geological CO2 sequestration (CCS) by injecting CO2 for the purpose of an enhanced geothermal system. The Qiabuqia geothermal field in China is selected as a study case to formulate the geothermal reservoir simulation. The results show that a pure CO2 injection into a water-saturated reservoir can provide the best performance in heat mining. Besides, this operational strategy can also provide extra benefits by producing 6.7% CO2 retention. The generated geothermal electricity under a pure CO2 injection into a CO2-saturated formation is the lowest, while its 42.1% of CO2 retention shows a promising CCS performance and the large volume of stored CO2 can supply some profits by carbon credit. Considering the assessment on heat mining and CCS, the pure CO2 injection into a water-saturated reservoir is recommended for the operation of an EGS. Under this flow strategy, well spacing, production pressure difference and fluid injection temperature are dominated in geothermal energy production. Three factors, including well spacing, production pressure difference and fracture conductivity, influence the CO2 storage capacity. In operating an EGS, a larger well spacing, a lower injection temperature and a lower fracture conductivity are suggested. While the optimal production pressure difference should be further determined to balance its effect on geothermal production and CO2 storage since it presents an opposite effect on these two parts. This work demonstrates the feasibility of heat mining associated with CO2 geological permanent storage in an EGS by injecting CO2. The proposed study proves that not only the sufficient and sustainable energy can be supplied but also a significant amount of CO2 emission can be eliminated simultaneously. In addition, the investigation of geothermal energy production and CO2 geological sequestration under different operational parameters can provide profound guidance for the operators.
- Asia > Middle East > Saudi Arabia (1.00)
- Asia > Middle East > Yemen (0.93)
- Africa > Sudan (0.93)
- (4 more...)
- Energy > Renewable > Geothermal > Geothermal Resource (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource for Power Generation > Enhanced Geothermal System (0.62)
Abstract Accurate modeling of CO2/CH4 competitive adsorption behavior is a critical aspect of enhanced gas recovery associated with CO2 sequestration in organic-rich shales (CO2-ESGR). It not only improves the ultimate recovery of shale gas reservoirs that satisfies the increasing energy demand but also provides permanent geologic storage of atmospheric CO2 that contributes to the net-zero energy future. Determining a CO2/CH4 adsorption ratio is essential for the performance prediction of shale gas reservoirs and the evaluation of CO2 storage potential. However, experimental adsorption measurements are expensive and time-consuming that may not always be available for shale reservoirs of interest or at the investigated geologic conditions, and as a result, a sorption ratio cannot be assessed appropriately. Traditional models such as a Langmuir model are highly dependent on extensive experiments and cannot be widely applied. Therefore, a unified adsorption model must be developed to predict the CO2/CH4 competitive adsorption ratios, which is essential for CO2 sequestration and exploitation of natural gas from shale reservoirs. In recent years, the development of machine learning algorithms has significantly improved the accuracy and computational speed of prediction. In this work, we conducted a comparative machine learning algorithm study to effectively forecast the maximum CO2 adsorption capacity and CO2/CH4 competitive adsorption ratios. Four sensitive input parameters (i.e., temperature, total organic carbon, moisture content, and maximum adsorption capacity of CH4) were selected, along with their 50 data points collected from the existing literature. The artificial neural network (ANN), XGBoost, and Random Forest (RF) algorithms were investigated. By comparing the mean absolute errors (MAE) and coefficients of determination (R), it was found that the ANN models can successfully forecast the required outputs within a 10% accuracy level. Furthermore, the descriptive statistics demonstrated that the CO2/CH4 competitive adsorption ratios were generally from 1.7 to 5.6. The proposed machine learning algorithm framework will provide insights beyond the isothermal conditions of classical adsorption models and the solid support to CO2-ESGR processes into which competitive adsorption can be a driven mechanism.
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- South America > Brazil > Brazil > South Atlantic Ocean (0.91)
Abstract Nowadays, the only economic and effective way to exploit shale reservoirs is multi-stage fracturing of horizontal wells. The backflow after fracturing affects the damage degree of a fracturing fluid to a formation and fracture conductivity, and directly influences a fracturing outcome. At present, the backflow control of the fracturing fluid mostly adopts empirical methods, lacking a reliable theoretical basis. Therefore, it is of positively practical significance to reasonably optimize a flowback process and control the flowback velocity and flowback process of a fracturing fluid. On the other hand, the previous research on the productivity of multi-stage fracturing horizontal wells after fracturing is limited, and an equation derivation process has been simplified and approximated to a certain extent, so its accuracy is significantly affected. Based on previous studies, this paper established a new mathematical model. This model optimizes the flowback velocity after fracturing by dynamically adjusting a choke size and analyzes and predicts the production performance after fracturing. To maximize fracture clean-up efficiency, this work builds the model for a dynamic adjustment of choke sizes as wellhead pressure changes over time. It uses a two-phase (gas and liquid) flow model along the horizontal, slanted and vertical sections. The forces acting on proppant particles, filtration loss of water, the compressibility of a fracturing fluid, wellbore friction, a gas slippage effect, water absorption and adsorption are simultaneously considered. With the theories of mass conservation, we build a mathematical model for predicting production performance from multi-fractured horizontal wells with a dynamic two-phase model considering dual-porosity, stress-sensitivity, wellbore friction, gas adsorption and desorption. In this model, the gas production mechanisms from stimulated reservoir volume and gas and water relative permeabilities are employed. Based on shale reservoir parameters, wellhead pressure, a choke size, a gas/liquid rate, cumulative gas/liquid production, cumulative filtration loss and a flowback rate are simulated. In the simulations, the influential factors, such as shut-in soak time of the fracturing fluid, forced flowback velocity, fracturing stages and fracture half-length after fracturing, are studied. It is found by comparison that in the block studied, when a well is shut in four days after fracturing, the dynamic choke size is adjusted with wellhead pressure changing over time, the fracturing stage is 11, and the fracture half-length is 350 meters, the fracture conductivity after flowback is the largest, and the productivity of the horizontal well is the highest.
- North America > United States > Texas (1.00)
- Asia (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- Asia > China > Sichuan > Sichuan Basin (0.99)
Predicting total organic carbon from well logs based on deep spatial-sequential graph convolutional network
Shan, Xiaocai (Chinese Academy of Sciences) | Chen, Zhangxin (China University of Petroleum (Beijing)) | Fu, Boye (Beijing University of Technology) | Zhang, Wang (Chinese Academy of Sciences, University of Chinese Academy of Sciences) | Li, Jing (China University of Petroleum (Beijing)) | Wu, Keliu (China University of Petroleum (Beijing))
ABSTRACT The total organic carbon (TOC) is a key geologic parameter for unconventional reservoirs. Conventional empirical methods cannot handle the nonlinear relationships between the characteristics of TOC and its well-log responses. Increased data availability has the potential to speed up deep learning applications, which can reasonably propagate the integrated information from well logs to indirectly observable geologic properties, such as TOC. Although the existing convolutional neural network (CNN) has found superior performance to for predicting TOC, CNNs feature-learning capability is still constrained by the fact that it can only extract log-specific sequential features of the input logs. However, the cross-log topological association features are potentially essential for the nonlinear mapping between well logs and TOC. Thus, we introduce a novel deep spatial-sequential graph convolutional network (SSGCN) for predicting the TOC by jointly leveraging the cross-log topological association features and log-specific sequential features. Through further use of the previously unaccounted topological interactions, our SSGCN dramatically outperforms the sequence-based CNN. In the southeast Sichuan Basin, SSGCN exhibits beneficial mapping not demonstrated previously: its models achieve a better cross-validation performance within the same gas field wells and a greater generalizability in another gas field well. Our SSGCN method can predict TOC of shale gas field well with the best being 0.87 within 1ย s on the CPU of a desktop computer, which increases the efficiency of obtaining the TOC parameter. From this study, we recommend graph and sequential convolutions for designing deep learning architectures in the well-log analysis.
- North America > Canada (1.00)
- Asia > Middle East > Iran (0.67)
- Asia > China > Sichuan Province (0.66)
- North America > United States > Texas > Dawson County (0.64)
- Geology > Geological Subdiscipline (1.00)
- Geology > Structural Geology > Tectonics (0.93)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.90)
- 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)
- North America > United States > Tennessee > Appalachian Basin (0.99)
- (28 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
An Integrated Geology-Engineering Approach to Duvernay Shale Gas Development: From Geological Modeling to Reservoir Simulation
Hui, Gang (Unconventional Petroleum Research Institute, China University of Petroleum, Beijing) | Chen, Zhangxin (Department of Chemical and Petroleum Engineering, University of Calgary) | Wang, Hai (Department of Chemical and Petroleum Engineering, University of Calgary) | Wang, Muming (Department of Chemical and Petroleum Engineering, University of Calgary) | Gu, Fei (Research Institute of Petroleum Exploration and Development, CNPC)
Abstract Unconventional shale resources are widely distributed in the Western Canadian Sedimentary Basin and have great potential for development. However, due to the complex distribution of shale sweet spots and the high drilling and fracturing costs of horizontal wells, the petroleum industry faces great challenges in the efficient and economic development of shale resources. This paper proposes an integrated geological-engineering method to characterize the Duvernay shale reservoir near the Fox Creek region. First, reservoir petrophysics is characterized based on core experiments. Second, based on geomechanical experiments and acoustic logging, we characterize elastic parameters and in-situ stress tensors to establish a geomechanical model. Next, focal mechanisms of microseismicity are employed to identify large-scale natural fractures and faults. Then, based on the aforementioned models, as well as the perforation and treatment data, the propagation of full 3D hydraulic fracture networks for horizontal wells is simulated to construct the unconventional fracture model (UFM) via Petrel Kinetix. Finally, numerical simulations of horizontal wells are conducted, which are further corroborated by the production performance of fractured wells. It is found that the core analysis of the key well suggests that reservoir porosity, permeability, and gas saturation are averaged to be 5.3% and 404 nD, respectively. The rock mechanical parameters, including Poisson's ratio, and Young's modulus, are derived from the triaxial compression tests, with both average values of 0.21 and 36.2 GPa, respectively. The natural fractures in the examined region have been demonstrated to be governed by two-period tectonic activities and hence developed with mean dip azimuths of NE21ยฐ and SE111ยฐ, respectively. Real-time fracturing parameters of four horizontal wells are used to simulate the complex propagation of hydraulic fracture networks, considering the reservoir heterogeneity and stress shadows among different stages. Numerical simulations of well production are conducted based on the geological and geomechanical models. The agreement between simulation results and production performance reaches more than 90%, indicating the effectiveness of this integrated method for shale gas development. This work provides a solid foundation for site selection and fracturing job size optimization of new horizontal wells in the future.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- North America > United States > Texas > Haynesville Shale Formation (0.99)
- North America > United States > Oklahoma > Arkoma Basin > Fayetteville Shale Formation (0.99)
- North America > United States > Louisiana > Haynesville Shale Formation (0.99)
- (8 more...)
Integration of Mineralogy, Petrophysics, Geochemistry and Geomechanics to Evaluate Unconventional Shale Resources
Hui, Gang (Unconventional Petroleum Research Institute, China University of Petroleum, Beijing, China) | Gu, Fei (PetroChina Research Institute of Petroleum Exploration and Development, CNPC) | Chen, Zhangxin (Department of Chemical and Petroleum Engineering, University of Calgary)
Abstract It is not entirely understood how related geological parameters vary during the thermal maturation and development of shale resources and controlling factors of shale productivity. Here, a detailed examination of mineralogy, geochemistry, petrophysics, and geomechanics-related data is conducted to explore the productivity of the Fox Creek, Alberta shale play. Experiments using X-Ray Diffraction, Tight Rock Analysis, Rock-Eval Pyrolysis, and Triaxial Compression are conducted to characterize the mineralogy, petrophysics, geochemistry, and geomechanics of the region under study. Multiple Linear Regression (MLR) is used to quantify the relationship between shale output productivity and reservoir input parameters. Using 300 core samples from 15 wells targeting the Duvernay shale, the key governing characteristics of shale potential were then examined. The Duvernay shale is dominated by quartz, clay, and calcite, according to X-Ray Diffraction measurements. Tight Rock Analysis indicates that the effective porosity of the Duvernay shale ranges from 1.56% to 6.11%, with an average value of 3.97 %, while the core permeability ranges from 0.25 to 345.5 nD, with an average value of 127.2nD. The total organic carbon (TOC) content ranged from 2.32 to 5.0 %, with an average of 3.86 %, according to Rock-Eval Pyrolysis testing. The majority of the Duvernay shale near the Fox Creek region (i.e., Fox Creek shale) was deposited in an oxygen-depleted maritime environment, whereas the Duvernay shale was in the gas generation window. The MLR technique determines the elements controlling shale productivity, including the production index, gas saturation, clay content, porosity, total organic carbon, brittleness index, and brittle mineral content as input parameters in decreasing order. Based on the MLR prediction model, the expected 12-month shale gas production per stage corresponds well with the actual value. This strategy can guide the future selection of horizontal well drilling sites and lead to the efficient and profitable development of shale resources.
- North America > Canada > Alberta (1.00)
- Asia > China (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geochemistry (1.00)
- North America > United States > Texas > Haynesville Shale Formation (0.99)
- North America > United States > Oklahoma > Arkoma Basin > Fayetteville Shale Formation (0.99)
- North America > United States > Louisiana > Haynesville Shale Formation (0.99)
- (37 more...)