Reservoir characterization
The industry is no longer short of measurements; the real challenge is converting them into timely reservoir decisions that protect value. That challenge is becoming more urgent as the industry depends increasingly on mature fields and existing infrastructure. The selected papers show how this need is being addressed across different producing regions.
This paper presents a case study from a mature field redevelopment project where pulsed neutron logging was integrated with advanced reservoir modeling to improve the understanding of fluid-contact dynamics and optimize new horizontal well placement.
This paper aims to showcase examples of how integrated analysis of surveillance data in the Azeri-Chirag-Gunashli field has improved reservoir understanding and informed reservoir-management decisions.
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SPE Data Science and Engineering Analytics Technical Director Silviu Livescu and SPE Reservoir Technical Director Rodolfo Camacho address some of the challenges in the application of data analytics, artificial intelligence, and machine learning to several reservoir engineering problems. -
The authors of this paper introduce a physics-based method for explicit pore-pressure prediction in naturally fractured shale petroleum reservoirs.
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The authors of this paper aim to identify the effect of various geologic controls on hydrocarbon maturation in the Delaware Basin by restoring regional cross sections and performing simulations of organic-matter maturity.
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This work applies an appropriate transient-test theory to demonstrate the applicability of each diagnostic plot along with clearly defining the characteristic features that allow each plot to be considered “diagnostic.”
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In the life cycle of a field, from exploration and production to development, as we hone our knowledge to understand the reservoir, we often explore various subdomains of formation evaluation at various scales. In this issue, we explore three such subdomains at three different scales.
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The authors of this paper describe an approach that combined active and passive near-field hydrophone measurements in a complex near-surface area offshore Abu Dhabi.
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The goal of this paper is to improve the understanding of uncertainties affecting well performance and reservoir connectivity in an offshore Malaysian field.
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The opening ceremony highlighted maximizing production sustainably to meet global demand, integration of simulation and optimization in a single platform with automation, and energy security.
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Large geological models are needed for modeling the subsurface processes in geothermal, carbon-storage, and hydrocarbon reservoirs. The size of these models contributes to the computational cost of history matching, engineering optimization, and forecasting. To reduce this cost, low-dimensional representations need to be extracted. Deep-learning tools, such as autoenc…
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This paper presents agile technologies that integrate data management, data-quality assessment, and predictive machine learning to maximize asset value using underused legacy core data.
