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Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- Research Report > New Finding (0.93)
- Overview (0.68)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Mineral (0.72)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- (2 more...)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- (20 more...)
Adaptive laterally constrained inversion of time-domain electromagnetic data using Hierarchical Bayes
Li, Hai (Chinese Academy of Sciences, Chinese Academy of Sciences) | Di, Qingyun (Chinese Academy of Sciences, Chinese Academy of Sciences) | Li, Keying (Chinese Academy of Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences)
Laterally constrained inversion (LCI) of time-domain electromagnetic (TEM) data is effective in recovering quasi-layered models, particularly in sedimentary environments. By incorporating lateral constraints, LCI enhances the stability of the inverse problem and improves the resolution of stratified interfaces. However, a limitation of the LCI is the recovery of laterally smooth transitions, even in regions unsupported by the available datasets. Therefore, we have developed an adaptive LCI scheme within a Bayesian framework. Our approach introduces user-defined constraints through a multivariate Gaussian prior, where the variances serve as hyperparameters in a Hierarchical Bayes algorithm. By simultaneously sampling the model parameters and hyperparameters, our scheme allows for varying constraints throughout the model space, selectively preserving lateral constraints that align with the available datasets. We demonstrated the effectiveness of our adaptive LCI scheme through a synthetic example. The inversion results showcase the self-adaptive nature of the strength of constraints, yielding models with smooth lateral transitions while accurately retaining sharp lateral interfaces. An application to field TEM data collected in Laizhou, China, supports the findings from the synthetic example. The adaptive LCI scheme successfully images quasi-layered environments and formations with well-defined lateral interfaces. Moreover, the Bayesian inversion provides a measure of uncertainty, allowing for a comprehensive illustration of the confidence in the inversion results.
- Geology > Mineral (0.93)
- Geology > Sedimentary Geology > Depositional Environment (0.34)
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Exmouth Plateau > WA-1-R > Scarborough Field (0.99)
- Europe > Norway (0.91)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (0.93)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.79)
- (2 more...)
ABSTRACT The explicit finite-difference (EFD) method is widely used in numerical simulation of seismic wave propagation to approximate spatial derivatives. However, the traditional and optimized high-order EFD methods suffer from the saturation effect, which seriously restricts the improvement of numerical accuracy. In contrast, the implicit FD (IFD) method approximates the spatial derivatives in the form of rational functions and thus can obtain much higher numerical accuracy with relatively low orders; however, its computational cost is expensive due to the need to invert a multidiagonal matrix. We derive an explicit strategy for the IFD method to reduce the computational cost by constructing the IFD method with the discrete Fourier matrix; then, we transform the inversion of the multidiagonal matrix into an explicit matrix multiplication; next, we construct an objective function based on the norm to reduce approximation error of the IFD method. This explicit strategy of the IFD method can avoid inverting the multidiagonal matrix, thus improving the computational efficiency. This constant coefficient optimization method reduces the approximation error in the medium-wavenumber range at the cost of tolerable deviation (smaller than 0.0001) in the low-wavenumber range. For the 2D Marmousi model, the root-mean-square error of the numerical results obtained by this method is one-fifth that of the traditional IFD method with the same order (i.e.,ย 5/3) and one-third that of the traditional EFD method with much higher orders (i.e.,ย 72). The significant reduction of numerical error makes the developed method promising for numerical simulation in large-scale models, especially for long-time simulations.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
ABSTRACT Although trial-and-error modeling may give some level of interpretation about the subsurface while sacrificing certainty, it is a viable alternative for precise 3D interpretation of real ground-airborne frequency-domain electromagnetic (GAFEM) data. In this sense, a semiautomatic trial-and-error modeling approach is developed. Specifically, we first develop the 3D GAFEM forward-modeling code. Its accuracy is demonstrated using a 3D synthetic model with topography and a tilted anomalous body. Second, an initial model is established based on known geologic constraints. Then, the code is conducted repeatedly, and the parameters of the model are renewed semiautomatically based on a predefined geometry-resistivity combination list. Finally, the model that can achieve the minimum error between the computed response and the collected GAFEM data is selected as the final model. Furthermore, we apply the presented semiautomatic trial-and-error modeling approach to the geothermal resources survey at the Yishu Faulting Basin, China. The purpose of the survey is to interpret the resistivity structure of the subsurface and evaluate the potential development of the geothermal resources in the survey area. As a result, the final model obtained by the trial-and-error modeling, which is constrained by the known geologic information and subsurface geoelectric structures inferred from 2D models inverted by the magnetotelluric and controlled-source audio-frequency magnetotelluric data measured at the same location, indicates the existence of the geothermal resources. This indication is proven by the drilling result of a well site located on the survey line. To further verify the reliability, a comparative analysis is conducted between the model obtained by the trial-and-error modeling and the models obtained by 3D inversion of a GAFEM data set and apparent resistivity calculation using the same data. The results indicate that different approaches can achieve similar subsurface geometry and resistivity distribution of the faulting basin structure.
- Asia > China (0.85)
- North America > Canada > Newfoundland and Labrador > Newfoundland (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource (0.65)
- North America > Canada > Saskatchewan > Athabasca Basin (0.99)
- North America > Canada > Alberta > Athabasca Basin (0.99)
- North America > Canada > Newfoundland and Labrador > Newfoundland > North Atlantic Ocean > Atlantic Margin Basin > Grand Banks Basin > Flemish Pass Basin (0.95)
- Asia > China > Shandong > Yishu Basin (0.95)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Non-Traditional Resources > Geothermal resources (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
ABSTRACT The explicit finite-difference (EFD) method is widely used in numerical simulation of seismic wave propagation to approximate spatial derivatives. However, the traditional and optimized high-order EFD methods suffer from the saturation effect, which seriously restricts the improvement of numerical accuracy. In contrast, the implicit FD (IFD) method approximates the spatial derivatives in the form of rational functions and thus can obtain much higher numerical accuracy with relatively low orders; however, its computational cost is expensive due to the need to invert a multidiagonal matrix. We derive an explicit strategy for the IFD method to reduce the computational cost by constructing the IFD method with the discrete Fourier matrix; then, we transform the inversion of the multidiagonal matrix into an explicit matrix multiplication; next, we construct an objective function based on the norm to reduce approximation error of the IFD method. This explicit strategy of the IFD method can avoid inverting the multidiagonal matrix, thus improving the computational efficiency. This constant coefficient optimization method reduces the approximation error in the medium-wavenumber range at the cost of tolerable deviation (smaller than 0.0001) in the low-wavenumber range. For the 2D Marmousi model, the root-mean-square error of the numerical results obtained by this method is one-fifth that of the traditional IFD method with the same order (i.e.,ย 5/3) and one-third that of the traditional EFD method with much higher orders (i.e.,ย 72). The significant reduction of numerical error makes the developed method promising for numerical simulation in large-scale models, especially for long-time simulations.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
ABSTRACT Although trial-and-error modeling may give some level of interpretation about the subsurface while sacrificing certainty, it is a viable alternative for precise 3D interpretation of real ground-airborne frequency-domain electromagnetic (GAFEM) data. In this sense, a semiautomatic trial-and-error modeling approach is developed. Specifically, we first develop the 3D GAFEM forward-modeling code. Its accuracy is demonstrated using a 3D synthetic model with topography and a tilted anomalous body. Second, an initial model is established based on known geologic constraints. Then, the code is conducted repeatedly, and the parameters of the model are renewed semiautomatically based on a predefined geometry-resistivity combination list. Finally, the model that can achieve the minimum error between the computed response and the collected GAFEM data is selected as the final model. Furthermore, we apply the presented semiautomatic trial-and-error modeling approach to the geothermal resources survey at the Yishu Faulting Basin, China. The purpose of the survey is to interpret the resistivity structure of the subsurface and evaluate the potential development of the geothermal resources in the survey area. As a result, the final model obtained by the trial-and-error modeling, which is constrained by the known geologic information and subsurface geoelectric structures inferred from 2D models inverted by the magnetotelluric and controlled-source audio-frequency magnetotelluric data measured at the same location, indicates the existence of the geothermal resources. This indication is proven by the drilling result of a well site located on the survey line. To further verify the reliability, a comparative analysis is conducted between the model obtained by the trial-and-error modeling and the models obtained by 3D inversion of a GAFEM data set and apparent resistivity calculation using the same data. The results indicate that different approaches can achieve similar subsurface geometry and resistivity distribution of the faulting basin structure.
- Asia > China (0.85)
- North America > Canada > Newfoundland and Labrador > Newfoundland (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource (0.65)
- North America > Canada > Saskatchewan > Athabasca Basin (0.99)
- North America > Canada > Alberta > Athabasca Basin (0.99)
- North America > Canada > Newfoundland and Labrador > Newfoundland > North Atlantic Ocean > Atlantic Margin Basin > Grand Banks Basin > Flemish Pass Basin (0.95)
- Asia > China > Shandong > Yishu Basin (0.95)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Non-Traditional Resources > Geothermal resources (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- North America > United States > Texas (1.00)
- Europe (0.93)
- Research Report > New Finding (0.93)
- Overview (0.88)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.47)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
When using forward modeling to estimate model parameters, such as the dip, it is also important to estimate the corresponding uncertainty in the model parameters. For gravity data, these uncertainties are dependent on the uncertainty in the Bouguer corrected data. The uncertainty in the gravity meter reading and the height used in the free-air and Bouguer corrections are amongst the most important factors influencing the uncertainty in the Bouguer-corrected data. We used two methods for estimating the uncertainty in the Bouguer corrected data, which give similar answers (0.121 and 0.109 mGal). The uncertainty in the model parameters can be estimated by perturbing the corrected data multiple times by amounts consistent with the estimated uncertainty in the corrected gravity. The standard deviation of the model parameters derived from each perturbed dataset gives an estimate of their uncertainty. Using this procedure for Bouguer gravity profiles that cross the Porcupine Destor fault (a fault that is prospective for gold in the Timmins camp of Ontario, Canada), we found the uncertainty in the dip was one or two degrees, assuming a planar or linear fault. If the uncertainty in the corrected data had been 1 mGal (a value typical of regional surveys, instead of 0.1 mGal for a local survey), then the uncertainty in the dip is 41 degrees for the same model. Knowing the uncertainties in the corrected data is thus very important for estimating the uncertainty in model parameters. Conversely, if a model parameter is known to be required to a specific precision, the survey can be planned so that the corrected gravity has an uncertainty appropriate to achieve that precision.
- Geology > Rock Type (0.68)
- Geology > Structural Geology (0.67)
- Geology > Sedimentary Geology (0.46)
- Geophysics > Gravity Surveying > Gravity Processing (0.88)
- Geophysics > Gravity Surveying > Gravity Acquisition (0.67)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Metals & Mining (0.93)
- North America > United States > California > Tisdale Field (0.89)
- Europe > Ireland > North Atlantic Ocean > Porcupine Basin > Druid Prospect (0.89)
- Europe > Ireland > North Atlantic Ocean > Porcupine Basin > Dromberg Prospect (0.89)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- (3 more...)
All material in this report, with accompanying figures, is property of SEG Advanced Modeling Corporation (SEAM). License to use the data and models can be obtained through SEAM. This document contains contributions from many different individuals and has been reviewed for accuracy. Reported errors will be fixed on a timely basis. The SEAM Carbonate model is the petroleum industry's first field-scale, digital model of a carbonate reservoir to be openly available.
- Geology > Rock Type > Sedimentary Rock (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Well Drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- (6 more...)
- Europe (1.00)
- Asia (1.00)
- North America > United States > Texas (0.67)
- Summary/Review (1.00)
- Instructional Material > Course Syllabus & Notes (1.00)
- Collection > Book (1.00)
- (2 more...)
- Geology > Structural Geology > Tectonics > Plate Tectonics (1.00)
- Geology > Rock Type > Sedimentary Rock (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- (5 more...)
- Energy > Oil & Gas > Upstream (1.00)
- Education > Educational Setting (1.00)
- Materials > Metals & Mining (0.92)
- Asia > Russia > Ural Federal District > Yamalo-Nenets Autonomous Okrug > Purovsky District > West Siberian Basin > Nadym-Pur-Taz Basin > Block V > Urengoyskoye Field > Achimov Formation (0.99)
- Asia > Russia > Ural Federal District > Yamalo-Nenets Autonomous Okrug > Purovsky District > West Siberian Basin > Nadym-Pur-Taz Basin > Block IV > Urengoyskoye Field > Achimov Formation (0.99)
- Asia > Russia > Ural Federal District > Yamalo-Nenets Autonomous Okrug > Purovsky District > West Siberian Basin > Nadym-Pur-Taz Basin > Block 5A > Urengoyskoye Field > Achimov Formation (0.99)
- (4 more...)