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Results
S-WAVE SEISMIC DATA INTERPRETATION FOR GAS RESERVOIR AT SANHU AREA, QAIDAM BASIN, WEST CHINA
Deng, Zhiwen (China National Petroleum Corporation (BGP INC)) | Zhang, Rui (University of Louisiana at Lafayette) | Wang, Yan (China National Petroleum Corporation (BGP INC)) | Yue, Yuanyuan (China National Petroleum Corporation (BGP INC)) | Xi, Xiaoyu (China National Petroleum Corporation (BGP INC)) | Wang, Xiusong (China National Petroleum Corporation (BGP INC)) | Wang, Jie (China National Petroleum Corporation (BGP INC))
The Qigequan Formation at the Sanhu area of the Qaidam Basin in western China is a significant gas production formation. However, the conventional P-wave seismic survey conducted in this region reveals the presence of extensive gas clouds that strongly attenuate P-waves, resulting in substantial uncertainty regarding the subsurface structure. To address this challenge, we undertook a 3D9C (three-dimensional nine-component) seismic survey, producing direct S-wave data unaffected by gas clouds, yielding remarkably clearer subsurface images with a higher level of confidence. The processing of the S-wave data largely utilized conventional P-wave processing techniques, except for shear wave splitting, which produced distinct Fast (S1) and Slow (S2) S-wave datasets. Notably, the S2 data exhibited superior quality compared to the S1 data, enabling us to apply various seismic attributes and inversion techniques to extract geological features. To validate our findings, we cross-referenced the seismic attributes and inversion results with well-log and production data, revealing a pronounced spatial correlation between the gas reservoir and channel structure. Consequently, we have identified channel structures as the prime targets for potential gas reservoirs.#xD;
- Geology > Structural Geology > Tectonics (1.00)
- Geology > Sedimentary Geology (1.00)
- Geology > Geological Subdiscipline > Stratigraphy (0.93)
- (2 more...)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.67)
- North America > United States > Colorado > DJ (Denver-Julesburg) Basin > Wattenberg Field > Niobrara Formation (0.99)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.99)
- Asia > China > Qinghai > Qaidam Basin (0.99)
Application of shear-wave seismic interpretation technology in shallow gas clouds area
Yan, Wang (BGP, CNPC) | Zhiwen, Deng (BGP, CNPC) | Zhigang, Chen (BGP, CNPC) | Yintao, Cai (BGP, CNPC) | Rui, Zhang (University of Louisiana at Lafayette) | Xiaoyu, Xi (BGP, CNPC) | Dan, Wu (BGP, CNPC)
Summary The imaging of conventional P-wave seismic data in gas cloud area is poor, which makes it impossible to use P-wave seismic data to describe structure and reservoir characteristics of gas fields. Shear wave is not significantly affected by pore fluid, which is more suitable for research in gas cloud area. Therefore, we explore the interpretation technology of S-wave seismic data, which is based on S-wave seismic acquisition and processing. Research shows that good application results have been achieved in the structure and reservoir of gas cloud area. Introduction 3D seismic survey is located in gas field.
- Geology > Rock Type (0.48)
- Geology > Geological Subdiscipline (0.32)
Direct S-wave seismic data interpretation for channel sand reservoir at Sanhu area, West China
Zhang, Rui (University of Louisiana at Lafayette) | Deng, Zhiwen (Bureau of Geophysical Prospecting Inc., China National Petroleum Corporation (BGP INC)) | Wang, Yan (Bureau of Geophysical Prospecting Inc., China National Petroleum Corporation (BGP INC)) | Xi, Xiaoyu (Bureau of Geophysical Prospecting Inc., China National Petroleum Corporation (BGP INC)) | Wang, Xiusong (Bureau of Geophysical Prospecting Inc., China National Petroleum Corporation (BGP INC)) | Wang, Jie (Bureau of Geophysical Prospecting Inc., China National Petroleum Corporation (BGP INC))
The Qigequan Formation at the Sanhu area of the Qaidam Basin in western China is a significant gas production formation. However, the conventional P-wave seismic survey conducted in this region reveals the presence of extensive gas clouds that strongly attenuate P-waves, resulting in substantial uncertainty regarding the subsurface structure. To address this challenge, we undertook a 3D9C (three-dimensional nine-component) seismic survey, producing direct S-wave data unaffected by gas clouds, yielding remarkably clearer subsurface images with a higher level of confidence. The processing of the S-wave data largely utilized conventional P-wave processing techniques, with the exception of shear wave splitting, which produced distinct Fast (S1) and Slow (S2) S-wave datasets. Notably, the S2 data exhibited superior quality compared to the S1 data, enabling us to apply various seismic attributes and inversion techniques to extract geological features. To validate our findings, we cross-referenced the seismic attributes and inversion results with well-log and production data, revealing a pronounced spatial correlation between the gas reservoir and channel structure. Consequently, we have identified channel structures as the prime targets for potential gas reservoirs.
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.50)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.90)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.99)
- Asia > China > Qinghai > Qaidam Basin (0.99)
Depth-domain angle and depth variant seismic wavelets extraction for prestack seismic inversion
Zhang, Rui (University of Louisiana at Lafayette) | Deng, Zhiwen (BGP Inc.)
ABSTRACT Many prestack depth migration methods have been developed and widely used to generate depth-domain seismic images, resulting in a need for depth-domain prestack seismic inversion of the subsurface elastic properties for reservoir characterization. Time-domain inversion techniques often are used after the depth-domain data set is transformed to the time domain. We provide a new technique for directly inverting depth-domain prestack seismic data for subsurface elastic properties: P-impedance (), S-impedance (), and density () in the depth domain. The proposed depth-domain workflow eliminates the need for time-depth/depth-time conversion, making it efficient and effective. Using a depth-wavenumber decomposition approach, the suggested workflow first extracts a collection of depth and angle varying wavelets to handle the potential nonstationarity of the depth-domain prestack seismic data. The extracted depth and angle variant wavelets are used in a basis pursuit inversion, which enhances the resolution of the inversion results. The workflow is tested on the Wenan 3D field data set, demonstrating its viability for practical applications.
- Asia > China (0.46)
- North America > United States (0.28)
- Research Report (0.46)
- Overview > Innovation (0.34)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
Ensemble Machine Learning for Predicting Viscosity of Nanoparticle-Surfactant-Stabilized CO2 Foam
Olukoga, Toluwalase (University of Louisiana at Lafayette (Corresponding author)) | Totaro, Micheal (University of Louisiana at Lafayette) | Feng, Yin (University of Louisiana at Lafayette)
Summary This paper investigates the computational behaviors of simple-to-use, relatively fast, and versatile machine learning (ML) methods to predict apparent viscosity, a key rheological property of nanoparticle-surfactant-stabilized CO2 foam in unconventional reservoir fracturing. The first novelty of our study is the investigation of the predictive performance of ML approaches as viable alternatives for predicting the apparent viscosity of NP-Surf-CO2 foams. The predictive and computational performance of five nonlinear ML algorithms were first compared. Support vector regression (SVR), K-nearest neighbors (KNN), classification and regression trees (CART), feed-forward multilayer perceptron neural network (MLPNN), and multivariate polynomial regression (MPR) algorithms were used to create models. Temperature, foam quality, pressure, salinity, shear rate, nanoparticle size, nanoparticle concentration, and surfactant concentration were identified as relevant input parameters using principal component analysis (PCA). A data set containing 329 experimental data records was used in the study. In building the models, 80% of the data set was used for training and 20% of the data set for testing. Another unique aspect of this research is the examination of diverse ensemble learning techniques for improving computational performance. We developed meta-models of the generated models by implementing various ensemble learning algorithms (bagging, boosting, and stacking). This was done to explore and compare the computational and predictive performance enhancements of the base models (if any). To determine the relative significance of the input parameters on prediction accuracy, we used permutation feature importance (PFI). We also investigated how the SVR model made its predictions by utilizing the SHapely Additive exPlanations (SHAP) technique to quantify the influence of each input parameter on prediction. This work’s application of the SHAP approach in the interpretation of ML findings in predicting apparent viscosity is also novel. On the test data, the SVR model in this work had the best predictive performance of the single models, with an R of 0.979, root mean squared error (RMSE) of 0.885 cp, and mean absolute error (MAE) of 0.320 cp. Blending, a variant of the stacking ensemble technique, significantly improved this performance. With an R of 1.0, RMSE of 0.094 cp, and MAE of 0.087 cp, an SVR-based meta-model ensembled with blending outperformed all single and ensemble models in predicting apparent viscosity. However, in terms of computational time, the blended SVR-based meta-model did not outperform any of its constituent models. PCA and PFI ranked temperature as the most important factor in predicting the apparent viscosity of NP-Surf-CO2 foams. The ML approach used in this study provides a comprehensive understanding of the nonlinear relationship between the investigated factors and apparent viscosity. The workflow can be used to evaluate the apparent viscosity of NP-Surf-CO2 foam fracturing fluid efficiently and effectively.
- Asia (1.00)
- North America > United States > California (0.46)
- North America > United States > Massachusetts (0.28)
Seismic interpretation of sandstone-type uranium deposits in the Songliao Basin, Northeast China
Hu, Shuang (Northeast Petroleum University) | Hu, Huiting (Northeast Petroleum University, Northeast Petroleum University) | Shi, Erxiu (Yanshan University) | Tang, Chao (Tianjin Center of China Geological Survey) | Zhang, Rui (University of Louisiana at Lafayette) | Hao, Yugang (Shaanxi Energy Institute)
Abstract Nuclear energy is a clean energy source that can replace fossil energy on a large scale, and the nuclear energy industry has great advantages in terms of its technical maturity, economic potential, and sustainability. The stable supply of uranium ore, a type of energy mineral, is an important factor for the sustainable development of the nuclear energy industry; as a result, uranium ore has increasingly become the focus of current mineral exploration. We have used seismic attribute analysis, stratal slicing, and seismic inversion to characterize the Sifangtai Formation’s sandstone-type uranium deposit in area A of the central Songliao Basin. We conclude that seismic attribute analysis and the stratal slicing method can predict the continuous reservoir sand body in the study area, and seismic inversion can describe the spatial features of the reservoir sand body in detail. Further analysis of uranium-bearing drilling finds that uranium ore is located in the middle sand body of the lower segment of the Sifangtai Formation, in the middle of the study area, and that the predicted reservoir sand body meets the high-precision requirements for exploration. We use the combination of seismic interpretation and drilling analysis of uranium-bearing ore bodies to effectively predict the size and shape of uranium-bearing ore bodies, and this combined approach can provide guidance for uranium exploration. Furthermore, the results of this study also are significant for the prediction of sand bodies in uranium reservoirs.
- Geology > Sedimentary Geology (1.00)
- Geology > Mineral (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.76)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (0.68)
- Materials > Metals & Mining > Uranium (1.00)
- Energy > Power Industry > Utilities > Nuclear (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- North America > United States > Illinois > Dupo Field (0.99)
- North America > United States > California > Sacramento Basin > 2 Formation (0.99)
- Europe > Norway > Norwegian Sea > Åre Formation (0.99)
- (12 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Health, Safety, Environment & Sustainability > Environment > Naturally occurring radioactive materials (1.00)
Prediction and Analysis of Geomechanical Properties using Deep Learning: A Permian Basin Case Study
Nath, Fatick (Texas A & M International University) | Murillo, Karina (Texas A & M International University) | Asish, Sarker Monojit (University of Louisiana at Lafayette) | Ganta, Deepak (Texas A & M International University) | Limon, Valeria (Texas A & M International University) | Aguirre, Edgardo (Texas A & M International University) | Aguirre, Gabriel (Texas A & M International University) | Debi, Happy R. (University of Louisiana at Lafayette) | Perez, Jose L. (Texas A & M International University) | Netro, Cesar (Texas A & M International University) | Borjas, Flavio (Texas A & M International University)
Abstract Unconventional reservoirs are continuously facing severe technical challenges in safe drilling, successful completion, and effective fracturing to unlock their potential. One of the key gaps is accurately estimating the geomechanical properties (Young's modulus, Poisson ratio, etc.) due to its complex structure, reservoir heterogeneity, and insufficient borehole information. While these properties could be calculated from sonic logs, it often results in log deficiency and a high recovery cost of incomplete datasets. To fill the gap accurately, we propose both classical machine learning and deep neural network to estimate and predict the geomechanical properties of the Permian Basin. The log-derived prediction algorithm includes (a) Single-Well prediction, 75% of log data of a single well is used as a specimen for training the Bi-LSTM, and the rest 25% of data of the same well is used for testing, and (b) Cross well prediction, a group of wells from the same region is divided into training and testing. The logs used in this work were collected from seven Permian Basin wells gamma-ray, bulk density, resistivity, etc. Finally, we employed four various machine learning (ML) algorithms (Decision Tree, Ada Boost, kNN, and Random Forest) to compare and investigate the efficacy of the deep neural network in predicting geomechanical properties. The results demonstrate promising predictions of the geomechanical properties for the Permian Basin using ML and deep neural networks. The highest performance for a single well prediction using the ML models with an R value of 99.90%. In a deep neural network, Bi-LSTM performs superior with an accuracy of 92.5%. The highest average accuracy obtained for single well prediction is 90.7%. Cross well prediction performed superior for all wells compared to the single well prediction and for both known and completely unknown data sets. While datasets are incomplete, these results demonstrate the excellent ability of machine learning models trained on adequate data sets to generate a realistic prediction. Given adequate training data, ML-based models will likely be able to fill the information gap by making an accurate prediction. Adoption of machine learning models for predicting geomechanical properties could have extensive impacts on both treatment design and the role of the geomechanicist. This research demonstrates the application of the Bi-LSTM model for the Permian Basin by accurately predicting geomechanical properties which can be used for both conventional and unconventional reservoirs to reduce cost and a considerable amount of time in completion workflows and thus increase the hydrocarbon recovery. Operators could leverage this promising deep learning model utilizing it as an automated handy tool to audit fracture interpretations quickly where datasets are incomplete.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.47)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (24 more...)
Study of the Effect of Wetting on Viscous Fingering Before and After Breakthrough by Lattice Boltzmann Simulations
Mora, Peter (King Fahd University of Petroleum and Minerals) | Morra, Gabriele (University of Louisiana at Lafayette) | Yuen, Dave (Columbia University and Ocean University of China, Qingdao) | Juanes, Ruben (Massachusetts Institute of Technology)
Abstract We present a suite of numerical simulations of two-phase flow through a 2D model of a porous medium using the Rothman-Keller Lattice Boltzmann Method to study the effect of viscous fingering on the recovery factor as a function of viscosity ratio and wetting angle. This suite involves simulations spanning wetting angles from non-wetting to perfectly wetting and viscosity ratios spanning from 0.01 through 100. Each simulation is initialized with a porous model that is fully saturated with a "blue" fluid, and a "red" fluid is then injected from the left. The simulation parameters are set such that the capillary number is 10, well above the threshold for viscous fingering, and with a Reynolds number of 0.2 which is well below the transition to turbulence and small enough such that inertial effects are negligible. Each simulation involves the "red" fluid being injected from the left at a constant rate such in accord with the specified capillary number and Reynolds number until the red fluid breaks through the right side of the model. As expected, the dominant effect is the viscosity ratio, with narrow tendrils (viscous fingering) occurring for small viscosity ratios with M ≪ 1, and an almost linear front occurring for viscosity ratios above unity. The wetting angle is found to have a more subtle and complicated role. For low wetting angles (highly wetting injected fluids), the finger morphology is more rounded whereas for high wetting angles, the fingers become narrow. The effect of wettability on saturation (recovery factor) is more complex than the expected increase in recovery factor as the wetting angle is decreased, with specific wetting angles at certain viscosity ratios that optimize yield. This complex phase space landscape with hills, valleys and ridges suggests the dynamics of flow has a complex relationship with the geometry of the medium and hydrodynamical parameters, and hence recovery factors. This kind of behavior potentially has immense significance to Enhanced Oil Recovery (EOR). For the case of low viscosity ratio, the flow after breakthrough is localized mainly through narrow fingers but these evolve and broaden and the saturation continues to increase albeit at a reduced rate. For this reason, the recovery factor continues to increase after breakthrough and approaches over 90% after 10 times the breakthrough time.
Application of automatic fault picking on the seismic data from Subei Basin, China by using optimal path voting method
Guo, Yu (University of Louisiana at Lafayette) | Zhang, Rui (University of Louisiana at Lafayette) | Hu, Huiting (Northeast Petroleum University)
Usually, a fault attribute which can enhance fault features is needed for detecting these sub-seismic faults. But most seismic attributes can be sensitive to noise and stratigraphic features, which are also apparent as reflection discontinuities within a seismic image. Therefore, in this article, we use methods of optimal surface voting (Wu and Fomel, 2018) that aims at enhancing features of faults and attenuating the influence of noise, stratigraphic characteristics, and other non-fault factors to automatic pick some sub-seismic faults in the study area.
- Asia > China > Jiangsu > Subei Basin (0.99)
- Asia > China > Jiangsu > North Jiangsu Basin (0.99)
- Asia > China > Jiangsu Basin (0.99)
TOC estimation of Tuscaloosa Marine Shale (TMS) using multiattribute analysis
Hoque, S M Shamsul (University of Louisiana at Lafayette) | Zhang, Rui (University of Louisiana at Lafayette)
Predicting high Total Organic Carbon (TOC) zones within an unconventional reservoir is very important to determine favorable geological areas for producing oil and gas. In this paper, we use combined analysis of seismic attributes and well logs for estimating probable TOC values among a portion of Tuscaloosa Marine Shale (TMS) formation. The TOC values in the well location have been calculated using Passey’s ΔLogR method which later used for multiattribute seismic analysis over the survey area. Using three seismic attributes, we have found a validation correlation coefficient of 81% while running stepwise regression method. With this accuracy, the predicted TOC volume among the formation has been established.
- North America > United States > Louisiana (0.78)
- North America > United States > Mississippi (0.77)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.94)
- Geophysics > Seismic Surveying > Seismic Processing (0.70)
- North America > United States > Mississippi > East Gulf Coast Tertiary Basin > Tuscaloosa Marine Shale Formation (0.99)
- North America > United States > Louisiana > East Gulf Coast Tertiary Basin > Tuscaloosa Marine Shale Formation (0.99)
- Asia > Middle East > Iran > Khuzestan > Zagros Basin > Mansuri Field > Khami Formation (0.99)
- (2 more...)