Estimation of in-situ horizontal stresses using the linear poroelastic model and minifrac test results in tectonically active area

Hayavi, Mohammad Tabaeh; Abdideh, Mohammad

doi:10.2205/2016es000576

Cited by 17 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The horizontal stresses can be estimated based on a poro-elastic approach (Equations ( 12) and ( 13)) [22].…”

Section: Horizontal Stressesmentioning

confidence: 99%

“…In the above equations, S hmin and S Hmax are minimum and maximum horizontal stress magnitudes, respectively, S v is vertical stress, E is Young's modulus, ν is Poisson ratio, α is Biot coefficient, P p is pore pressure, and ε H and ε h are the maximum and minimum horizontal strain magnitudes. Appropriate values for the two horizontal strains and, hence, the two horizontal stress magnitudes, can be found by fitting these values to observational data on in situ stress magnitudes, e.g., from extended leak-off tests and minifracs [22].…”

Section: Horizontal Stressesmentioning

confidence: 99%

See 1 more Smart Citation

Building 1D and 3D Mechanical Earth Models for Underground Gas Storage—A Case Study from the Molasse Basin, Southern Germany

Zain-ul-Abedin

Henk

2020

Energies

View full text Add to dashboard Cite

Hydromechanical models of gas storage in porous media provide valuable information for various applications ranging from the prediction of ground surface displacements to the determination of maximum reservoir pressure and storage capacity to maintain fault stability and caprock integrity. A workflow to set up such models is presented and applied to a former gas field in southern Germany for which transformation to a gas storage site is considered. The workflow comprises 1D mechanical earth modeling (1D MEM) to calculate elastic properties as well as a first estimate for the vertical and horizontal stresses at well locations by using log data. This information is then used to populate a 3D finite element model (3D MEM) which has been built from seismic data and comprises not only the reservoir but the entire overburden up to the earth’s surface as well as part of the underburden. The size of this model is 30 × 24 × 5 km3. The pore pressure field has been derived from dynamic fluid flow simulation through history matching for the production and subsequent shut-in phase. The validated model is ready to be used for analyzing new wells for future field development and testing arbitrary injection-production schedules, among others.

show abstract

“…The horizontal stresses can be estimated based on a poro-elastic approach (Equations ( 12) and ( 13)) [22].…”

Section: Horizontal Stressesmentioning

confidence: 99%

Section: Horizontal Stressesmentioning

confidence: 99%

Building 1D and 3D Mechanical Earth Models for Underground Gas Storage—A Case Study from the Molasse Basin, Southern Germany

Zain-ul-Abedin

Henk

2020

Energies

View full text Add to dashboard Cite

show abstract

“…Third, the seismic prediction technology of geostress in a deep shale reservoir is immature. The magnitude and orientation of geostress are the main factors for predicting the geostress field (Hayavi and Abdideh, 2016). The geostress azimuth prediction technology based on wide-azimuth seismic data is immature, and the seismic prediction technology for geostress size has not fully considered the reservoir heterogeneity, making the prediction accuracy low.…”

Section: Introductionmentioning

confidence: 99%

Research on geophysical response analysis and prediction technology of geostress in the shale gas area of the southern Sichuan Basin

Wang,

Yin,

Shi

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

The exploration and development potential of shale gas reservoirs in the Sichuan Basin is enormous; however, it also faces difficulties such as complex structures, strong heterogeneity, and unclear geophysical response characteristics. Fine prediction of geostress is an important part of shale gas exploration and development, which directly affects the implementation effect of reservoir evaluation, well trajectory design, and fracture reconstruction. The existing geostress prediction techniques lack high-precision seismic data constraints, making it difficult to accurately reflect the planar distribution characteristics of geostress in the block with rapid changes in complex tectonic zones. At the same time, the geophysical response characteristics of geostress in the Sichuan Basin are unknown, and the geostress seismic prediction technology lacks theoretical basis. This paper combines numerical simulation and physical experiments and defines the characteristics of the geophysical response of shale gas reservoirs in the Sichuan Basin changing with the stress field, and technical countermeasures for geostress seismic prediction have been established to provide technical means for accurate prediction of the geostress field in the shale gas block. Based on the geostress sensitive parameters obtained from prestack seismic inversion, the geostress field prediction of a shale gas work area in the Sichuan Basin is realized.

show abstract

“…After extensive testing of rocks strata of known ratios, it was determined that the neural networks were as effective as preexisting models in determining not on the modulus ratios, but several other pertinent measurements (unconfined compressive strength, modulus of elasticity, et cetera) in known wells. This was dependent upon the operational functions utilized as inputs, however, the after the network was trained, few errors could be produced (Havavi et al, 2016).…”

Section: Neural Network Used For Geomechanics Rocks and Stresses: -mentioning

confidence: 99%

Using Artificial Neural Networks to Predict Formation Stresses for Marcellus Shale with Data from Drilling Operations

AbuSurra¹

View full text Add to dashboard Cite

Artificial neural networks have been applied to different petroleum engineering disciplines. This is contributed to the powerful prediction capability in complex relationships with enough data available. The objective of this study is to develop a new methodology to predict the vertical and horizontal stresses using artificial neural networks for Marcellus shale well laterally drilled in Monongalia County, WV. This approach coupled the drilling surface measurements with the recorded well logging data. Drilling parameters included depth, WOB, RPM, standpipe pressure, torque, pump flow rate and rate of penetration. Well logging data included gamma ray and bulk density. The model output was the minimum horizontal stress and vertical stress. The well trajectory was divided into two main parts, the vertical and lateral section since the change in the drilling direction along with changing structural geology and sedimentation impacted the resultant stresses. Several neural networks were designed with different number of feedforward backpropagation architectures. The collected data was filtered and normalized before neural networks were trained using part of data. A percentage of the data was used to validate the trained model. Finally, a blind data set aside was used to test the model prediction accuracy and to estimate error percentages. Preliminary results show that adding logging data such as gamma ray and bulk density improves the model accuracy. Also, increasing the number of hidden layers and neurons improved the efficiency. However, higher the number of neurons and hidden layers higher was the computational cost due to increased model convergence time. The correlation coefficients of the predicted and observed values ranged between 0.76 and 0.99. This approach is beneficial regarding hydraulic fracturing design and fracture orientation prediction in unconventional shales. iii Dedication Dedicated to my dear God, without Him guiding me in every step, nothing would be possible for me. This is also dedicated to my father Salem Abusurra who's presence would make this achievement even more special. Also, I would like to present this to my beloved mother Fatima Omar Mousa whose constant sacrifices give me a better life and led me to this point. iv ACKNOWLEDGEMENT I would like to express my deepest thanks and appreciations to my research advisor Dr. Ilkin Bilgesu for his continuous support and encouragement throughout this research. His contributions are numerous and valuable. I would also like to thank all members of my research committee for their guidance and helpful suggestions. I heartily appreciate the support and advice from Professor Sam Ameri, Chair of Petroleum and Natural Gas Engineering Department. He has been a father and friend throughout my study. My thank is to my academic advisor Dr. Kashy Aminian for his support and advice and special thanks to all family of Petroleum and Natural Gas Engineering Department especially to Ms. Beverly Matheny for her friendly ambiance and continuous help. My sincere appr...

show abstract

Estimation of in-situ horizontal stresses using the linear poroelastic model and minifrac test results in tectonically active area

Cited by 17 publications

References 14 publications

Building 1D and 3D Mechanical Earth Models for Underground Gas Storage—A Case Study from the Molasse Basin, Southern Germany

Building 1D and 3D Mechanical Earth Models for Underground Gas Storage—A Case Study from the Molasse Basin, Southern Germany

Research on geophysical response analysis and prediction technology of geostress in the shale gas area of the southern Sichuan Basin

Using Artificial Neural Networks to Predict Formation Stresses for Marcellus Shale with Data from Drilling Operations

Contact Info

Product

Resources

About