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In most engineering and scientific applications, machine learning (ML) or artificial intelligence (AI) methods in general, are primarily oriented to design a statistical/heuristic procedure to predict the outcome of a system under new conditions. This mechanism aims at exploring non-evident correlations between inputs and outputs that are embedded in the data. However, a large body of this effort relies on black-box function approximations (e.g., neural networks) that have shown limitations to elucidate additional insights from the underlying physical process that generated the data. Thus, this type of knowledge is generated in a data-driven manner without fully explaining the physics governing the problem.
In this work, a novel analysis approach using an artificial intelligence (AI) framework that automatically helps identify the drivers of a formulation from lab measurements was showcased. This AI framework builds and optimizes models in the form of physics-based equations from small amounts of measurement data. With this approach, the user can overcome the large data requirements of machine learning while building tailored models that outperform traditional analytical and statistical tools.The effectiveness of this modeling framework in helping scientists reduce uncertainty early in the experiment process was demonstrated. This solution allows a significant reduction in the number of experiments required to achieve an optimal formulation. This was accomplished by generating new, custom models from existing data and well-known equations in electrochemistry. Then, these models were used to predict or hypothesize the performance of unseen formulations by altering their control parameters. This study showed the accuracy of these predictions by calculating its error against unseen measurement data.
The Wafra Joint Operation (WJO) Oilfield is located in the central-west part of the Kuwait-Saudi Arabia Neutral Zone. The Wafra oilfield reserves were first discovered and wells drilled in 1953 and production in commercial quantities began in 1954. This field produces two types of crude oil, Ratawi (light oil) and Eocene (heavy oil), with average water cut of 80-85%. During operation, the production wells produce the oil emulsion through mostly coated flowlines to sub-centres (SC) where the sour oil, water and gas are separated. The facility has two gathering fields: Eocene and Ratawi. Eocene has 2 phase separation, whilst Ratawi has 3 phase separation.
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Over the past two decades, bio-based fuel-grade ethanols (BFGEs), derived from a variety of agriculture feedstocks (e.g., corn, sugar cane, soybean oil, and sugar beet), are increasingly being used as a renewable energy source to reduce the dependence of fossil fuels for motor vehicle applications. One cost-effective and environmentally benign way to transport BFGEs is through steel transmission pipelines. However, cases of environmentally assisted cracking (EAC) in the transportation of BFGEs have been documented including some in pipelines.
This production asset located in the deep-water offshore Brazil, producing heavy oil in the range of 16 to 24 oAPI. Mudline caisson separators with electrical submersible pumps (ESPs) are used to process fluids from multiple wells and boost them to the receiving floating production, storage, and offloading (FPSO) vessel(1). There are significant flow-assurance and corrosion challenges in operating the asset. One of the challenges is the production fields have limited subsea umbilical, necessitate the use ofmultifunctional products to maintain the field’s integrity and mitigate any flow assurance and scale issues.