Case-Based Reasoning Approach for Well Failure Diagnostics and Planning

Abstract

Case-based reasoning, also known as computer reasoning by analogy, is a simple and practical technique that solves new problems by comparing them to ones that have already been solved in the past, thus saving time and money. The technique constantly incorporates dynamic data, which empowers the system to learn and adapt from new experiences. A general framework for case-based reasoning is presented, along with a review of the four-step cycle that characterizes the technology: retrieve, reuse, revise and retrain.This paper presents a specific application of case-based reasoning to determine the optimum cleaning technique (bailing, washback, or foaming) for sanded/seized well failures. The methodology extracts only the most relevant information from the historical database, utilizes a rule-based system to make adaptations, and then suggests the most appropriate solution for a given well intervention. This technique was used for production operations as a front end tool for well workover planning and design and was applied to sample data from a large oil field where the main artificial lift system is rod pump. This simple case demonstrates how case-based reasoning can be applied to improve the planning and execution of well interventions, thus reducing cost, rig time, and well turnaround time, while maximizing reliability and production.

Planning pipeline pigging operations with predictive maintenance

“…This is an advancement from an early stage when only the real-time parameters fitting the stability range and need for maintenance were evaluated. Other examples, such as [43,44] include the profuse employment of casebased reasoning for unveiling diagnostics of well failures based on the previous knowledge, which most often is too complex to be directly applied without AI techniques.…”

A review of technological advances and open challenges for oil and gas drilling systems engineering

“…102 After generating a dataset of specific offshore drilling information using fuzzy logic, Mendes et al 103 implemented a genetic algorithm for the prediction of feasible trajectories for wells and directional drilling parameters using retrieved datasets of similar drilling scenarios. Popa et al 104 applied case-based reasoning (an AI technique) for the selection of the optimum hole cleaning procedure in unconsolidated sands by collating datasets from nearly 5000 wells; an accuracy of 80% between AI proposed methods and those actually implemented was observed. Wang et al 105 applied an artificial neural network model developed by British Petroleum (BP) for the optimal selection of deep-water floating platforms (a decision dependent on many interconnected variables).…”

A Real‐Time Decision Support System for High Cost Oil Well Drilling Operations

“…Popa et al (Popa et al 2008) applied CBR to determine the optimum cleaning technique for filter failures. A small subset of historical cases was taken from the database to evaluate the proposed solution with the actual results.…”

“…Project documents, well summary documents, and technical lesson documents are three levels of documents in the knowledge base hierarchy. Popa et al (2008) applied CBR to determine the optimum cleaning technique for filter failures. A small subset of historical cases was taken from the database to evaluate the proposed solution with the actual results.…”