Formation evaluation
As several commercial-scale projects are gearing up in the US, developers expect the next few years to reveal whether lithium extraction from brines can become a big business.
This paper presents a novel approach to predict reservoir porosity by conditioning seismic data, calibrating seismic impedance inversion, and tailoring rock-physics analysis.
Smart Seismic Solutions plans to use 50,000 nodes to examine storage potential for the Greenstore CCS project.
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The SPE Reservoir Technical Discipline and Advisory Committee invite their Reservoir members worldwide to participate in a new survey aimed at assessing the current state of reservoir engineering across industry and academia. Deadline is 21 July 2025. -
This work investigates the root cause of strong oil/water emulsion and if sludge formation is occurring within the reservoir using a robust integrated approach.
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Transitioning to a low-carbon economy demands large-scale CO2, natural gas, and hydrogen storage. In this context, the application of AI/ML technology to uncover geochemical, microbial, geomechanical, and hydraulic mechanisms related to storage and solve complicated history-matching and optimization problems, thereby enhancing storage efficiency, has been prominently …
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The service giant shares new details about its automated fracturing spreads that slash human operator workload by 88%.
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Regional pore-pressure variations in the Leonardian- and Wolfcampian-age producing strata in the Midland and Delaware basins are studied using a variety of subsurface data.
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This paper presents a novel workflow for using electromagnetic resistivity-based reservoir mapping logging-while-drilling technologies for successful well placement and multilayer mapping in low-resistivity, low-contrast, thinly laminated clastic reservoirs.
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This paper details how the reservoir modeling workflow can be accelerated, and uncertainty reduced, even for challenging greenfield prospects by constructing multiple small fit-for-purpose integrated adaptive models.
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The authors of this paper propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment.
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This paper develops a deep-learning work flow that can predict the changes in carbon dioxide mineralization over time and space in saline aquifers, offering a more-efficient approach compared with traditional physics-based simulations.
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The authors of this paper propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs.
