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Collaborating Authors
Modeling & Simulation
Numerous surface-felt earthquakes have been spatiotemporally correlated with hydraulic fracturing operations. Because large deformations occur close to hydraulic fractures (HFs), any associated fault reactivation and resulting seismicity must be evaluated within the length scale of the fracture stages and based on precise fault location relative to the simulated rock volumes. To evaluate changes in Coulomb failure stress (CFS) with injection, we conducted fully coupled poroelastic finite-element simulations using a pore-pressure cohesive zone model for the fracture and fault core in combination with a fault-fracture intersection model. The simulations quantify the dependence of CFS and fault reactivation potential on host-rock and fault properties, spacing between fault and HF, and fracturing sequence. We find that fracturing in an anisotropic in-situ stress state does not lead to fault tensile opening but rather dominant shear reactivation through a poroelastic stress disturbance over the fault core ahead of the compressed central stabilized zone. In our simulations, poroelastic stress changes significantly affect fault reactivation in all simulated scenarios of fracturing 50-200 m away from an optimally oriented normal fault. Asymmetric HF growth due to the stress-shadowing effect of adjacent HFs leads to 1.) a larger reactivated fault zone following simultaneous and sequential fracturing of multiple clusters compared to single-cluster fracturing; and 2.) larger unstable area (CFSgt;0.1) over the fault core or higher potential of fault slip following sequential fracturing compared to simultaneous fracturing. The fault reactivation area is further increased for a fault with lower conductivity and with a higher opening-mode fracture toughness of the overlying layer. To reduce the risk of fault reactivation by hydraulic fracturing under reservoir characteristics of the Barnett Shale, the Fort Worth Basin, it is recommended to 1.) conduct simultaneous fracturing instead of sequential; and 2.) to maintain a minimum distance of ~ 200 m for HF operations from known faults.
- North America > Canada (1.00)
- North America > United States > Texas > Travis County > Austin (0.28)
- North America > United States > Texas > Tarrant County > Fort Worth (0.24)
- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (1.00)
- Geology > Structural Geology > Fault (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- (2 more...)
- South America > Argentina > Patagonia > Neuquรฉn > Neuquen Basin > Vaca Muerta Shale Formation (0.99)
- North America > United States > Wyoming > Green River Basin > Jonah Field (0.99)
- North America > United States > West Virginia > Appalachian Basin (0.99)
- (51 more...)
Sensitivity analysis of S-waves and their velocity measurement in slow formations from monopole acoustic logging-while-drilling
Ji, Yunjia (University of Electronic Science and Technology of China, Guilin University of Electronic Technology, Chinese Academy of Sciences) | Wang, Hua (University of Electronic Science and Technology of China, University of Electronic Science and Technology of China)
Monopole acoustic logging-while-drilling (LWD) enables the direct measurement of shear (S) wave velocity in slow formations, which has been corroborated by recent theoretical and experimental studies. However, this measurement is hampered by the weakness of the S-wave signal and the lack of techniques to amplify it. To address this challenge, we have analytically computed the monopole LWD wavefields, considering both centralized and off-center tools in various slow formations. Modeling analysis reveals that four parameters primarily influence the excitation of the formation S-wave: the formation S-wave velocity, the source-to-receiver distance, the radial distance from receiver to wellbore, and source frequency. S-wave signals can be enhanced by judiciously optimizing these parameters during tool design. Furthermore, our research suggests that the S-wave velocity can be accurately extracted through the slowness-time correlation method only when formation S-wave velocities are in a suitable range. This is because an overly high S-wave velocity causes shear arrivals to be interfered with the inner Stoneley mode, whereas an ultra-slow formation S-wave velocity results in S-wave signals too faint to detect. For the LWD model with an off-center tool, simulations demonstrate that tool eccentricity, especially large eccentricity, can amplify the shear wave and improve its measurement accuracy, provided that waveforms received in the direction of tool movement are used. In a very slow formation, we successfully extracted the S-wave velocity from synthetic full-wave data at that azimuth under conditions of large eccentricity, a task not achievable with a centralized instrument.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (0.87)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Well Drilling > Drilling Measurement, Data Acquisition and Automation > Logging while drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- North America > United States > California (1.00)
- Europe (1.00)
- Asia (1.00)
- (2 more...)
- Geology > Structural Geology > Tectonics > Plate Tectonics (1.00)
- Geology > Rock Type (1.00)
- Geology > Mineral (1.00)
- (3 more...)
- Geophysics > Gravity Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying (0.92)
- (2 more...)
- Materials > Metals & Mining (1.00)
- Materials > Chemicals (1.00)
- Government > Regional Government > North America Government > United States Government (1.00)
- (5 more...)
- North America > United States > Nevada > Dixie Valley Field (0.99)
- North America > United States > California > Mayacamas Mountains > Geysers Field (0.99)
- North America > Trinidad and Tobago > Trinidad > Southern Basin (0.99)
- (3 more...)
- Information Technology > Modeling & Simulation (0.92)
- Information Technology > Communications > Collaboration (0.40)
Cloud-based connected workflows enable us to dramatically improve, automate, accelerate, and simplify the subsurface modeling process.In this episode, SLB discusses agile reservoir modeling, a key example of connected workflows, and shows how it can transform subsurface studies and derive operational insights, enabling us to make better decisions faster and at reduced risk.
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)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.67)
- Information Technology > Modeling & Simulation (0.67)
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)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.67)
- Information Technology > Modeling & Simulation (0.67)
Achieving global net-zero emissions goals will be impossible without CCUS. To be successful, CCUS project teams must meet time and cost constraints while addressing three major engineering stages: 1) CO2 Capture, 2) Transportation 3) Geological Storage. In this webinar, we will focus on Geological Storage modeling & simulation capabilities. In general, Geological formations for CO2 storage must have sufficient capacity and injectivity as well as the ability to confine the lateral or vertical migration of CO2 to the surface. By attending this webinar, you will learn how multiphysics and multiscale simulation, including geomechanics simulation with SIMULIA Abaqus and pore-scale simulation with SIMULIA DigitalROCK, a virtual rock lab, are used to avoid costly mistakes and project delays by assessing CO2 storage capacity, injectivity and containment.
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Communications > Web (1.00)
Modern and efficient reservoir management is imperative, given the ever-increasing demand for oil. Making the right decision on reservoir development utilizing all available data in a timely manner is the key to a successful operation. For mature reservoirs, this requires high-quality uncertainty assessment of long-term performance forecast estimations. One critical and difficult component of the total uncertainty in forecasting is the one that stems from the implicit uncertainty in the geological and reservoir simulation models. In fact, regardless of the amount of reservoir data that we collect, there is no way to define the reservoir model uniquely.
- Information Technology > Modeling & Simulation (0.61)
- Information Technology > Artificial Intelligence > Machine Learning > Forecasting (0.40)
The oil and gas industry uses static and dynamic reservoir models to assess volumetrics and to help evaluate development options via production forecasts. The models are routinely generated using sophisticated software. Elegant geological models are generated without a full understanding the limitations imposed by the data or the underlying stochastic algorithms. Key issues facing reservoir modelers that have been evaluated include use of reasonable semivariogram model parameters (a measure of heterogeneity), model grid size, and model complexity. However, reservoir forecasts tend to be optimistic โ a statement not provable with data in the public domain. Yet, conversations at technical meetings, the lack of industry publications highlighting actual forecast accuracy, the development of more detailed reservoir models (presumably to yield better forecasts), all suggest that the industry could improve its reservoir performance forecast accuracy.