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Collaborating Authors
Middle East
Embracing a mission of connecting the world of applied geophysics,the Society of Exploration Geophysicists (SEG) is a not-for-profit organization supporting more than 11,000 members from 126 countries. SEG's long-standing tradition of excellence in education, professional development, new business generation, and engagement cultivates a unique community platform that encourages collaboration and thought leadership for the advancement of geophysical science around the world. Headquartered in Houston, TX, SEG has a business office in Tulsa, OK and regional offices in Dubai, UAE and Beijing, China. SEG is a global society that fosters the expert and ethical practice of geophysics in the exploration and development of natural resources, in characterizing the near-surface, and in mitigating earth hazards by inspiring the geophysicists of today and tomorrow.
- North America > United States > Texas > Harris County > Houston (0.30)
- North America > United States > Oklahoma > Tulsa County > Tulsa (0.30)
- Asia > Middle East > UAE > Dubai Emirate > Dubai (0.30)
- Asia > China > Beijing > Beijing (0.30)
- Information Technology > Knowledge Management (0.82)
- Information Technology > Communications > Collaboration (0.82)
On cost-efficient parallel iterative solvers for 3-D frequency-domain seismic multi-source viscoelastic anisotropic wave modeling
Ma, Guoqi (Khalifa University of Science and Technology) | Zhou, Bing (Khalifa University of Science and Technology) | Riahi, Mohamed Kamel (Khalifa University of Science and Technology) | Zemerly, Jamal (Khalifa University of Science and Technology) | Xu, Liu (King Fahd University of Petroleum and Minerals)
Solving large sparse linear systems in 3-D frequency-domain seismic wave modeling, especially in viscoelastic anisotropic media, poses significant challenges due to the increasing number of discrete moduli and nonzero elements in the linear system matrix. The computational load surpasses that of acoustic or viscoacoustic media, making it even more challenging when dealing with multi-source problems. Popular scientific tools for solving a linear system like MUMPS, STRUMPACK, and PETSc can be utilized, but their applicability to our specific problem has not been comprehensively evaluated. Our study aims at tackling the challenges in solving large sparse, complex-valued symmetric linear systems with multiple right-hand-side vectors for 3-D frequency-domain seismic wave modeling. We have leveraged preconditioned conjugate gradient iterative algorithms as the foundation for our research, introducing two highly cost-effective parallel iterative solvers: the Parallel Symmetric Successive Over-Relaxation Conjugate Gradient (P-SSORCG) and the Parallel Incomplete Cholesky Conjugate Gradient (P-ICCG). These novel solvers were subjected to a comprehensive comparative analysis against well-established scientific tools, including MUMPS, STRUMPACK, and PETSc, in the context of 3-D frequency-domain seismic wave modeling. We show their promising performances in a practical 3-D SEG/EAGE overthrust model and demonstrate that the grouped P-SSORCG offers an efficient alternative to parallel direct solvers, particularly in situations where computational resources are limited.
Stress-dependent reflection and transmission of elastic waves under confining, uniaxial, and pure shear prestresses
Yang, Haidi (China University of Petroleum (East China)) | Fu, Li-Yun (China University of Petroleum (East China), Pilot National Laboratory for Marine Science and Technology (Qingdao)) | Mller, Tobias M. (China University of Petroleum (East China)) | Fu, Bo-Ye (Beijing University of Technology)
Insights into the reflection and transmission (R/T) of waves at a prestressed interface are important in geophysical applications, such as evaluating the angle-dependent elastic properties for monitoring geopressure and tectonic stress using sonic logging data or seismic data. Although many studies deal with wave propagation in prestressed media, the angle-dependent R/T of waves at an interface subject to different prestress loading modes remain largely unaddressed. We intend to address this issue by applying the theory of acoustoelasticity with third-order acoustoelastic constants to study the R/T coefficients at the interface between two prestressed media. Stress-induced elastic deformations are assumed to be locally homogeneous without boundary dislocations caused by stress concentration so that the static boundary conditions can be applied. We consider three typical prestress modes (confining, uniaxial, and pure shear), each of which takes into account the incidence of upgoing and downgoing P and S waves. The Knott equations under different types of prestresses are derived, followed by estimating of angle-dependent R/T coefficients. The energy conservation at the interface and the acoustoelastic finite-difference simulation of predicted P and S modes verify the correctness of the angle-dependent R/T coefficients under confining prestress. Comparisons with the elastic case (prestress =0MPa) show the important influence of prestresses on the energy distribution of R/T waves, including stress-dependent critical angles, converted waves, and R/T energy ratios. Such acoustoelastic effects mainly occur around/after the critical angle. For small-angle incidence, prestresses mainly affect the gradient of R/T coefficients. Both the type and magnitude of prestress are closely related to the angle-dependent R/T coefficients and must be considered for amplitude-variation-with-offset (AVO) analysis in prestressed media.
- Asia > China (0.28)
- North America > United States (0.28)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
The Abu Dhabi National Oil Company (ADNOC) announced this week that its implementation of artificial intelligence (AI) technologies generated an additional half-billion dollars in value last year. The company credited more than 30 AI programs for contributing to the gains, highlighting their impact across its entire value chain. ADNOC also reported that these AI initiatives helped to prevent the emission of up to a million metric tons of CO2 from 2022 to 2023. "Artificial intelligence is one of the most important economic and social game changers of our era, and it can play a crucial role in accelerating a just, orderly, and equitable energy transition," Sultan Ahmed Al Jaber, CEO of ADNOC, stated. "At ADNOC, we have integrated artificial intelligence across our operations, from the control room to the boardroom, and it is enabling us to make smarter decisions and better protect our people and the environment."
- Government > Regional Government > Asia Government > Middle East Government > UAE Government (1.00)
- Energy > Oil & Gas (1.00)
The Abu Dhabi National Oil Company (ADNOC) has announced that it generated 500 million in value by deploying artificial intelligence (AI) in 2023. The value was generated from the integration of more than 30 AI tools across ADNOC's value chain, the company said. Additionally, the company said, these applications abated up to 1 million tonnes of carbon dioxide emissions between 2022 and 2023, the equivalent of removing around 200,000 gasoline-powered cars from the road. The milestone marks the start of the company's multiyear program to accelerate the deployment of AI to enhance safety, while driving down emissions and driving up value. "Artificial intelligence is one of the most important economic and social game changers of our era, and it can play a crucial role in accelerating a just, orderly and equitable energy transition," said Sultan Ahmed Al Jaber, the UAE's minister of industry and advanced technology and the managing director and group CEO of ADNOC.
- Government > Regional Government > Asia Government > Middle East Government > UAE Government (1.00)
- Energy > Oil & Gas (1.00)
LITHOCODIUM MOUND IDENTIFICATION USING LWD IMAGE LOG AND QUANTIFIED CUTTING ANALYSIS ย VALIDATION WITH ANALOGUES
Perrin, Christian (North Oil Company) | Pointer, Chay (North Oil Company) | Al-Mohannadi, Ghada (North Oil Company) | Sen, Shantanu (North Oil Company) | Buraimoh, Muse Ajadi (QatarEnergy)
Lithocodium mounds are early Cretaceous sedimentary structures described in the literature from outcrops, however, never described in the subsurface. The objective of this work is to identify and characterize Lithocodium mounds in the subsurface along a 25,000ft horizontal well. Drill cuttings sampled at a 100ft interval are observed in thin sections to define and quantify key sedimentary indicators (bioclasts, facies, and texture). Logging-while-drilling (LWD) GR, density, neutron, and resistivity logs are acquired along with the LWD high-resolution borehole image (BHI) log. Bedding dips from BHI data, interpreted along the horizontal well, enabled the reconstruction of the reservoir paleotopography. In particular, the alternation of dip azimuth combined with the facies interpretation from the thin sections supported the interpretation of eight distinct mound structures. An assessment of their overall geometry confirmed the mound shape to be subcircular, consistent with the subcircular geometries observed in Oman at the outcrop. The inferred dimensions of the mounds are comparable with the Aptian Lithocodium mounds in Oman (3040m), and their intermound organization resembles that of the Albian mounds in Texas. This work demonstrates the value of analyzing cuttings to complement image log interpretation and the value of outcrop analogs for interpreting sedimentary structures. For the first time, the subsurface identification and characterization of Lithocodium mounds and intermounds are achieved.
- North America > United States > Texas (0.48)
- Asia > Middle East > Oman (0.45)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Sedimentary Geology > Depositional Environment (0.93)
- Geology > Geological Subdiscipline > Stratigraphy (0.66)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.48)
- Well Drilling > Drilling Operations (1.00)
- Well Drilling > Drilling Measurement, Data Acquisition and Automation > Logging while drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Embracing a mission of connecting the world of applied geophysics,the Society of Exploration Geophysicists (SEG) is a not-for-profit organization supporting more than 11,000 members from 126 countries. SEG's long-standing tradition of excellence in education, professional development, new business generation, and engagement cultivates a unique community platform that encourages collaboration and thought leadership for the advancement of geophysical science around the world. Headquartered in Houston, TX, SEG has a business office in Tulsa, OK and regional offices in Dubai, UAE and Beijing, China. SEG is a global society that fosters the expert and ethical practice of geophysics in the exploration and development of natural resources, in characterizing the near-surface, and in mitigating earth hazards by inspiring the geophysicists of today and tomorrow.
- North America > United States > Texas > Harris County > Houston (0.30)
- North America > United States > Oklahoma > Tulsa County > Tulsa (0.30)
- Asia > Middle East > UAE > Dubai Emirate > Dubai (0.30)
- Asia > China > Beijing > Beijing (0.30)
- Information Technology > Knowledge Management (0.82)
- Information Technology > Communications > Collaboration (0.82)
In recent years, large language models (LLMs) have become increasingly popular in natural language processing (NLP) research. The recent advancements in LLMs have revolutionized the field of natural language processing (NLP). LLMs are trained on massive amounts of text data and can generate human-like text with impressive accuracy. They have shown remarkable performance in various NLP tasks such as text classification, question answering, and language generation. Two of the most popular large language models are Generative Pre-trained Transformer 3 and 4 (GPT-3 & GPT-4), which were developed by OpenAI.
Join us for this exclusive interview with Reem Al Ghanim, Head of HR and Support Services--Chemical Division at Saudi Aramco, as we explore the impacts of COVID-19 and the low-carbon era on recruitment and the makeup of the future workforce. What does diversity look like at Saudi Aramco? This interview will be moderated by Trent Jacobs, Digital Editor, the Society of Petroleum Engineers.
- Government > Regional Government > Asia Government > Middle East Government > Saudi Arabia Government (1.00)
- Energy > Oil & Gas (1.00)