Focused Reservoir Fluid Sampling Uses Artificial Intelligence Technology

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 210091, “Optimizing Focused Reservoir Fluid Sampling Using a Deterministic Causation Artificial Intelligence Intuition Technology,” by Rabindra Chakraborty, Senslytics; Chengli Dong, Shell; and Hani Elshahawi, SPE, NoviDigiTech, et al. The paper has not been peer reviewed. _ 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. The method uses a special probing tool containing two distinct flow areas. The central (sample) inlet is connected to a sample line, often with an associated pump, while the peripheral (guard) ring is also associated with a dedicated pump. Focused sampling usually results in faster cleanup but comes with increased operational complexity. The flow rate of the sample and guard lines must be properly adjusted and synchronized for each case at hand, and the interpretation of downhole measurements on both sides is significantly more complex than for conventional sampling operations. The success of fluid-sampling operations is highly dependent on human interpretations of the real-time downhole sensor data. Focused sampling is carried out by manipulating the differential rates of sample and guard pumps to stimulate and accelerate sample cleanup. Various types of flow regimes observed during focused sampling operations may be classified as follows: - Idle: During such regimes, both sample and guard pumps are turned off. Thus, no cleanup takes place, and fluids in the tool’s sample lines that are detected by the downhole analyzers remain stagnant. - Commingled: During commingling, either the sample pump or the guard pump is running, but flow occurs through both the sample and guard areas combined. - Split flow: In such periods, both sample and guard pumps run simultaneously but at different flow rates, with fluids moving at different speeds through the sample (central) and guard (outer) areas. The physics of fluid flow in near-wellbore porous media are highly complex and challenging to compute analytically and difficult to numerically simulate at a relevant scale, especially in the context of real-time operations. The authors have developed what they term “intuition technology,” an artificial-intelligence paradigm that uses scientific learning as its fabric and mimics the human decision-making process by iterating hypotheses and weighing situational changes surrounding dynamics simultaneously while keeping an eye on possible asymptomatic behavior. The process involves harnessing experts’ subtle knowledge of fluid properties and change behavior as hypotheses and iterating them with the situational and time-series data to generate a scientific fabric that governs the cleanup. A contamination-forewarning application built on this intuition framework was able to interpret the fluid-cleanup state successfully in all examples examined as part of this pilot.