Operators face many challenges when selecting well-intervention candidates and evaluating a field’s potential because the process is highly time consuming, labor intensive, and susceptible to cognitive biases. An operator can lose up to USD 10 million/year because of ineffective well-intervention strategies in a single field. The objective of this study is to reduce such losses and standardize the well-intervention process by intelligently using the domain knowledge with artificial-intelligence (AI) and machine-learning (ML) techniques. The workflow developed in this study can automatically and autonomously analyze the surface-subsurface data to expeditiously recommend the top intervention candidates. The workflow leverages proven petroleum-engineering methods and customizable business logic to identify underperforming wells and then recommend workover techniques, post-workover production, success probability, and profitability. It uses production, petrophysics, reservoir, and economics data to run a series of AI/ML techniques. The data-analytics engine runs k-nearest neighbors to predict post-workover rates, followed by a decision tree to identify the remedies. Artificial neural network, random forest, and Monte-Carlo simulation are adapted to identify new perforation opportunities in existing wells. Analytic hierarchy process ranks the top intervention candidates based on post-workover rate, permeability, remaining reserves, and reservoir-production trends. Finally, Bayesian belief network calculates the probability of success. With this implementation, the manual benchmarking process of opportunity identification, which usually takes weeks to months, can now be completed within minutes. Once the opportunity is identified and reviewed, it gets registered in the opportunity tracker list for the final evaluation by the asset team. The results are displayed on web-based applications with customizable dashboards and can be integrated with any existing online/offline systems. Because the whole process is now automated and takes very little execution time, petroleum engineers can review the field’s performance on a daily basis. With more than 80% predictive accuracy and 90% time saving compared to the manual process, this workflow presents a step-change in the operator’s well-intervention management capacity. In this paper, the authors discuss the adaptations to the industry-standard AI/ML algorithms and the best practices to provide a faster, more accurate, and efficient well-intervention advisory system.

In this paper, the authors will discuss a systematic approach to digitalize field surveillance and identify Production Enhancement (PE) opportunities by incorporating dynamic Well Operating Envelopes (WOE). The approach considers multiple components of the producing well's technical restrictions and constraints such as reservoir management, downhole completions, tubing/piping erosion and surface production facility. It is always a challenge to operate production wells daily as it involves multiple factors such as reservoir depletion, formation damage, and/or aging equipment. The failure of not being able to recognize and control well production behaviors may lead the producers unable to meet production targets and other severe issues like well integrity. In the oil and gas industry, well performance management is a vital component of optimizing production systems. Hence, WOE must be accurately defined to maintain asset integrity as well as reasonable production forecasts from available resources. The digital solutions include the development of prescriptive model-based technical workflows that employs a visualization tool to graphically represent the WOE with integrated performance dashboards to enable informed and optimal decision making. The solution leverages traditional petroleum engineering analyses and continuously enriched lookback knowledge base workflow combined with proven business logic to automatically and autonomously: Identify underperforming wells through their performance signatures.Check the quality of multi-disciplinary input data and engineering models integrated with a digital ecosystem to ensure the back-end solution engine can generate valued information for actionable recommendations.Predict potential concerns to ensure the producers are functioning in a safe and stable manner.Determine root causes and recommend appropriate remedial actions/opportunities to optimize production performance.Probabilistically quantify production gain, evaluate economic viability, and estimate the chance of success. This method has been used in several fields and wells with various completion types and field-wide constraints, and it has proven to be flexible enough to accommodate the possible differences between well types and field peculiarities. The case study presented in this paper will demonstrate some of the benefits realized including improved reservoir management and optimization opportunities identified (i.e. flowline pressure debottlenecks, reservoir stimulation, gaslift valve change, well bean-up, behind casing opportunity, etc.). In addition, the visualization tool has been used for exception-based surveillance (EBS), which has proven to improve our response time resulting in better deferment management. Furthermore, the visualization tool has been used to carry out exception-based well surveillance that has proven to improve our response time on well deviations for better deferment management. The collaborative approach between Operator and Solution Partner has enabled digitalization of field surveillance and PE candidate identification for an effective and efficient Reservoir, Well and Facility Management (RWFM) to protect the base production and maximize asset value within the safe limits on a day-to-day basis.