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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