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Aria Abubakar was born in Bandung, Indonesia. He received an MSc degree in Electrical Engineering in 1997 and a PhD in Technical Sciences in 2000, both from the Delft University of Technology, The Netherlands. He joined Schlumberger-Doll Research in Ridgefield, CT, USA in 2003, where he remained for 10 years, ending his tenure as a Scientific Advisor and the Manager of the Multi-Physics Modeling and Inversion Program. From 2013 until mid-2017, he was the Interpretation Engineering Manager at Schlumberger Houston Formation Evaluation in Sugar Land, TX. From mid-2017 until mid-2020, he was Data Analytics Program Manager for Software Technology and then Head of Data Science for the Schlumberger Exploration and Field Development Platform based in Houston, TX. Aria is currently the Head of Data Science for the Digital Subsurface Solutions.
- Europe > Netherlands > South Holland > Delft (0.25)
- North America > United States > Texas > Harris County > Houston (0.25)
- North America > United States > Texas > Fort Bend County > Sugar Land (0.25)
- (2 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
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)
Feasibility of Using the Frequency Domain Electromagnetic Method to Detect Pipeline Blockages: Experimental Study
Zhang, Chengli (Guizhou University) | Pan, Jianwei (Guizhou University, Guizhou University) | Liu, Jiaxu (Guizhou University) | Zhan, Lin (Guizhou University) | Gao, Jian (Guizhou University) | Luo, Haixin (Guizhou University) | Yang, Chen (Guizhou University)
Normal function of modern society requires a vast number of buried pipelines with differing specifications, which could become blocked to varying degrees during routine operation. Consequently, a major technical municipal engineering problem is how best to accurately, nondestructively, and efficiently locate blockages in underground pipelines. The frequency domain electromagnetic method (FDEM) has been widely used in geophysical exploration because of its features of nondestructive examination and high efficiency. In considering application of FDEM to the problem of locating blockages in underground pipelines, numerical simulations and physical experiments were conducted on different pipeline types with different blocked states, different blockage substances, and different working configurations. Results demonstrated that FDEM is most applicable to detection of blockages in insulated pipelines, especially when the single-ended charging method is used or when the blockage has reasonable impermeability, because the induction signal exhibits a particularly obvious abrupt drop at the blockage position. However, owing to the low resistivity of metals and the current propagating to the surrounding ground, which makes it easier to form more conductive paths than blockages, the FDEM displays no obvious change in signal characteristics at the position of blockage in a metal pipeline, which means that this method has certain challenges for detecting such blockages.
- Research Report > New Finding (0.64)
- Research Report > Experimental Study (0.64)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.93)
- Facilities Design, Construction and Operation > Pipelines, Flowlines and Risers > Piping design and simulation (0.66)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.58)
Colorado School of Mines and the US Geological Survey (USGS) announced they will partner to establish a joint industry program to explore the potential of geologic hydrogen as a low-carbon energy source. Eight companies including BP, Chevron, and Petrobras, have signed on as industry partners to help fund the program. The consortium's research will focus on the development of four key areas: A geologic "hydrogen system" model that identifies sources, migration pathways and mechanisms, reservoirs, traps, and seals leading to accumulations of hydrogen in the subsurface. Surface exploration approaches, including remote sensing and surface geochemistry, to refine our understanding of where hydrogen accumulations exist in the subsurface. Subsurface exploration tools, including multiple geophysical tools, advanced signal processing and artificial intelligence tools, to image geologic hydrogen systems and potential economic accumulations suitable for energy production.
- Energy > Renewable > Hydrogen (1.00)
- Government > Regional Government > North America Government > United States Government (0.98)
- Reservoir Description and Dynamics > Fluid Characterization (0.66)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.62)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.46)
Join us for an SPE Live episode on "Innovation in Hydraulic Fracturing" with a preview of the SPE Hydraulic Fracturing Technology Conference and Exhibition on 6-8 February 2024, being held in the Woodlands, Texas โ USA. A Q&A session will then follow with questions from the audience, answered by our guests.
- Well Completion > Hydraulic Fracturing (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.49)
AI has been around for about five decades. However, in the past few months due to emergence of Generative AI, there is a renewed buzz about Artificial Intelligence, and its applications for all businesses. As exciting are the opportunities, there are also concerns about managing AI effectively, the need for legal and regulatory safeguards, and more use cases. Today we want to briefly explore "How generative AI can create value for the energy industry?". Our panelists share their insights, and we have a more detailed panel discussion at the ATCE at DSEATS dinner speaker event on 16 October 2023 in San Antonio, Texas.
Artificial intelligence (AI) is increasingly being employed to assist in the development of materials, including metal-organic frameworks (MOFs), to develop carbon capture technologies. MOFs are modular materials made up of three building blocks: inorganic nodes such as zinc or copper; organic nodes; and organic linkers made up of carbon, oxygen, and other elements. By changing the relative positions and configurations of the building blocks, the potential combinations for creation of unique MOFs are countless. The idea is to create a porous carbon dioxide "trap" to capture carbon from the air. The structure created by the building blocks can be thought of simplistically as a scaffolding with joints (linkers) that functions to absorb carbon.
- Energy > Power Industry (0.32)
- Government > Regional Government (0.31)
- Reservoir Description and Dynamics (1.00)
- Health, Safety, Environment & Sustainability > Environment > Climate change (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Deep carbonate reservoir characterization using multiseismic attributes: A comparison of unsupervised machine-learning approaches
Zhao, Luanxiao (Tongji University) | Zhu, Xuanying (Tongji University) | Zhao, Xiangyuan (SINOPEC, Petroleum Exploration and Production Research Institute) | You, Yuchun (SINOPEC, Petroleum Exploration and Production Research Institute) | Xu, Minghui (Tongji University) | Wang, Tengfei (Tongji University) | Geng, Jianhua (Tongji University)
ABSTRACT Seismic reservoir characterization is of great interest for sweet spot identification, reservoir quality assessment, and geologic model building. The sparsity of the labeled samples often limits the application of supervised machine learning (ML) for seismic reservoir characterization. Unsupervised learning methods, in contrast, explore the internal structure of data and extract low-dimensional features of geologic interest from seismic data without the need for labels. We compare various unsupervised learning approaches, including the linear method of principal component analysis (PCA), the manifold learning methods of t-distributed stochastic neighbor embedding and uniform manifold approximation and projection (UMAP), and the convolutional autoencoder (CAE), on the 3D synthetic and field seismic data of a deep carbonate reservoir in southwest China. On the synthetic data, the low-dimensional features extracted by UMAP and CAE provide a better indication of porosity and gas saturation than traditional seismic attributes. In particular, UMAP better preserves the global structure of geologic features and indicates the potential of decoupling the gas saturation and porosity effects from seismic responses. We demonstrate that joint use of several types of seismic attributes, instead of using a single type of seismic attributes, can better delineate the reservoir structures using unsupervised ML. On the field seismic data, UMAP can effectively characterize the sedimentary facies distribution, which is consistent with the geologic understanding. Nevertheless, the porosity and saturation can not be reliably identified from field seismic data using unsupervised ML, which is likely caused by the complex pore structures in carbonates complicating the mapping relationship between seismic responses and reservoir parameters.
- North America > United States > Texas > Yoakum County (0.75)
- North America > United States > Louisiana (0.75)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Geological Subdiscipline (0.93)
- South America > Brazil > Brazil > South Atlantic Ocean > Santos Basin (0.99)
- North America > Mexico > Veracruz > Veracruz Basin (0.99)
- North America > Mexico > Gulf of Mexico > Veracruz Basin (0.99)
- Asia > China > Sichuan > Sichuan Basin (0.99)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Unsupervised or Indirectly Supervised Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.88)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
Probabilistic physics-informed neural network for seismic petrophysical inversion
Li, Peng (University of Wyoming) | Liu, Mingliang (Stanford University) | Alfarraj, Motaz (King Fahd University of Petroleum and Minerals, King Fahd University of Petroleum and Minerals) | Tahmasebi, Pejman (Colorado School of Mines) | Grana, Dario (University of Wyoming)
ABSTRACT The main challenge in the inversion of seismic data to predict the petrophysical properties of hydrocarbon-saturated rocks is that the physical relations that link the data to the model properties often are nonlinear and the solution of the inverse problem is generally not unique. As a possible alternative to traditional stochastic optimization methods, we develop a method to adopt machine-learning algorithms by estimating relations between data and unknown variables from a training data set with limited computational cost. We develop a probabilistic approach for seismic petrophysical inversion based on physics-informed neural network (PINN) with a reparameterization network. The novelty of our approach includes the definition of a PINN algorithm in a probabilistic setting, the use of an additional neural network (NN) for rock-physics model hyperparameter estimation, and the implementation of approximate Bayesian computation to quantify the model uncertainty. The reparameterization network allows us to include unknown model parameters, such as rock-physics model hyperparameters. Our method predicts the most likely model of petrophysical variables based on the input seismic data set and the training data set and provides a quantification of the uncertainty of the model. The method is scalable and can be adapted to various geophysical inverse problems. We test the inversion on a North Sea data set with poststack and prestack data to obtain the prediction of petrophysical properties. Compared with regular NNs, the predictions of our method indicate higher accuracy in the predicted results and allow us to quantify the posterior uncertainty.
- Asia > Middle East > Saudi Arabia (0.28)
- North America > United States > Colorado (0.28)
- North America > United States > California (0.28)
- (4 more...)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.94)
- Geology > Geological Subdiscipline > Geomechanics (0.89)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation > Seismic Reservoir Characterization > Amplitude vs Offset (AVO) (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.88)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.66)
Abstract Seismic fault interpretation is a critical task for any type of energy industry. Correct fault mapping can be crucial for the success of a project. Common geometric seismic attributes, such as coherence and curvature, are routinely employed to enhance fault visualization in seismic data. However, they can show limitations for subseismic faulting. In this study, we highlight the usefulness of including novel aberrancy attributes for fault identification in multiattribute analysis and unsupervised machine learning (ML) techniques. We compare broadband coherence, curvature, multispectral coherence, and aberrancy when trying to map faults in a potential CO2 storage location. We also compare self-organizing maps and generative topographic mapping techniques when including and excluding aberrancy attributes. Our results show that integrating aberrancy attributes during multiattribute analysis and ML steps considerably enhanced the visualization of lineaments with strikes similar to those of fracture sets seen only with well-log data and that were not clearly captured by the conventional seismic attributes and ML scenarios excluding aberrancy attributes. We demonstrate the potential of these novel geometric seismic attributes to map subseismic faults. We also provide an example that can encourage interpreters to include them in their interpretation workflows.
- Geology > Structural Geology > Fault (0.95)
- Geology > Geological Subdiscipline (0.69)
- Geology > Structural Geology > Tectonics (0.68)
- Geology > Rock Type > Sedimentary Rock (0.68)
- Oceania > New Zealand > North Island > Taranaki Basin (0.99)
- North America > United States > Oklahoma > Anadarko Basin > Cana Woodford Shale Formation (0.99)
- North America > United States > Alaska > North Slope Basin > Prudhoe Bay Field (0.99)
- (3 more...)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Health, Safety, Environment & Sustainability > Environment > Climate change (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)