Abstract
A practical framework that outlines the critical steps of a successful process that uses data, machine learning (Ml), and artificial intelligence (AI) is presented in this study. A practical case study is included to demonstrate the process. The use of artificial intelligent and machine learning has not only enhanced but also sped up problem-solving approaches in many domains, including the oil and gas industry. Moreover, these technologies are revolutionizing all key aspects of engineering including; framing approaches, techniques, and outcomes. The proposed framework includes key components to ensure integrity, quality, and accuracy of data and governance centered on principles such as responsibility, equitability, and reliability. As a result, the industry documentation shows that technology coupled with process advances can improve productivity by 20%.
A clear work-break-down structure (WBS) to create value using an engineering framework has measurable outcomes. The AI and ML technologies enable the use of large amounts of information, combining static & dynamic data, observations, historical events, and behaviors. The Job Task Analysis (JTA) model is a proven framework to manage processes, people, and platforms. JTA is a modern data-focused approach that prioritizes in order: problem framing, analytics framing, data, methodology, model building, deployment, and lifecycle management. The case study exemplifies how the JTA model optimizes an oilfield production plant, similar to a manufacturing facility. A data-driven approach was employed to analyze and evaluate the production fluid impact during facility-planned or un-planned system disruptions. The workflows include data analytics tools such as ML&AI for pattern recognition and clustering for prompt event mitigation and optimization.
The paper demonstrates how an integrated framework leads to significant business value. The study integrates surface and subsurface information to characterize and understand the production impact due to planned and unplanned plant events. The findings led to designing a relief system to divert the back pressure during plant shutdown. The study led to cost avoidance of a new plant, saving millions of dollars, environment impact, and safety considerations, in addition to unnecessary operating costs and maintenance. Moreover, tens of millions of dollars value per year by avoiding production loss of plant upsets or shutdown was created. The study cost nothing to perform, about two months of not focused time by a team of five engineers and data scientists. The work provided critical steps in "creating a trusting" model and "explainability’. The methodology was implemented using existing available data and tools; it was the process and engineering knowledge that led to the successful outcome. Having a systematic WBS has become vital in data analytics projects that use AI and ML technologies. An effective governance system creates 25% productivity improvement and 70% capital improvement. Poor requirements can consume 40%+ of development budget. The process, models, and tools should be used on engineering projects where data and physics are present.
The proposed framework demonstrates the business impact and value creation generated by integrating models, data, AI, and ML technologies for modeling and optimization. It reflects the collective knowledge and perspectives of diverse professionals from IBM, Lockheed Martin, and Chevron, who joined forces to document a standard framework for achieving success in data analytics/AI projects.
Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology
