储层表征

使用人工智能技术进行集中储层流体采样

本文的作者描述了一种基于因果关系的人工智能框架构建的技术,旨在预警化学、生物和地质系统中复杂、难以检测的状态变化。

核心数据和环数据处理。
图 1——更多和环形数据处理。

使用电缆地层测试 (WFT) 收集的样本可在油藏的整个生命周期中提供重要信息。受污染的样品可能会导致错误的流体分析结果,并可能造成巨大的经济后果。需要一种可以帮助工程师准确推断流体污染状态的应用程序。完整的论文描述了 WFT 污染预警应用程序的开发,该应用程序基于一个框架,该框架提供有关流体污染状态的实时决策建议,并提出有助于优化 WFT 操作的变更建议。

聚焦流体采样

在过去的十年中,集中流体采样已成为传统地层流体采样的可行替代方案。

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原文链接/jpt
Reservoir characterization

Focused Reservoir Fluid Sampling Uses Artificial Intelligence Technology

The authors of this paper describe a technology built on a causation-based artificial intelligence framework designed to forewarn complex, hard-to-detect state changes in chemical, biological, and geological systems.

Core- and ring-data processing.
Fig. 1—Core- and ring-data processing.

Samples collected using wireline formation testing (WFT) provide vital information throughout the lifetime of a reservoir. Contaminated samples can lead to erroneous fluid analysis results with potentially huge economic consequences. A need exists for an application that can assist engineers in accurately inferring the state of fluid contamination. The complete paper describes the development of a WFT contamination-forewarning application based on a framework that advises real-time decisions regarding the state of fluid contamination and recommending changes that will help optimize the WFT operation.

Focused Fluid Sampling

During the past decade, focused fluid sampling has emerged as a viable alternative to conventional formation-fluid sampling.

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