Reservoir simulation
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 paper presents a fundamental research study with the main objective of building a mechanistic numerical model that captures the important mechanisms of polymer flooding through various mechanistic equations using a combined reservoir flow and geochemical numerical simulator.
The authors of this paper describe reservoir-fluid-geodynamics processes that explain the reasons behind varying oil compositions and properties within and across different reservoir compartments.
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Producers face a number of decision-making challenges. Specifically, they must optimize field development and operational decisions in light of the complex interplay of fiscal, market, and reservoir variables. -
The story of unconventional oil and gas technology development has been focused on fractures. The formula has been more stages, more sand, and more water, targeting the most productive spots.
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The optimization algorithm used in this work is a hybrid genetic algorithm (HGA), which is the combination of GAs with artificial neural networks (ANNs) and evolution strategies (ESs).
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Reservoir-simulation-model inputs are numerous, and uncertainty is pervasive—before, during, and after development. With the pressure to deliver results quickly, how do we find the right balance?
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In upstream oil and gas, cloud computing is very immature because the industry has always been challenged by storage and computational capability. However, high-performance cloud computing may create an opportunity for smaller companies lacking infrastructure for scientific applications.
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Because the uncertainty analysis is complex and time consuming, in this paper, a stochastic representation of the computer model, called an emulator, was constructed to quantify the reduction in the parameter input space.
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With the easy conventional oil in Argentina having been produced, one remaining way to find new oil in existing fields is to convert fields from primary or secondary production to secondary or tertiary production, respectively.
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This paper describes how seismic reservoir integration, advanced production analysis, and accurate nanoscale and 3D full-field simulations may address profitability issues and help oil companies to be more efficient in developing unconventional portfolios.
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Computational advances in reservoir simulation have made possible the simulation of thousands of reservoir cases in a practical time frame. This enables exhaustive exploration of subsurface uncertainty and development/depletion options.
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Permanent downhole gauges (PDGs) can provide a continuous record of flow rate and pressure, which provides extensive information about the reservoir. In this work, a machine-learning framework based on PDG data was extended to two applications: multiwell testing and flow-rate reconstruction.
