Accelerate Intervention Planning, Decision Making, and Execution by Leveraging Digital Solutions

Abstract Well interventions play a key role in supporting growing energy needs and lowering carbon footprint by increasing recovery from the field. Despite an increased number of interventions, success rates have remained stagnant, mainly due to limitations of existing manual, custom-made processes, and lack of ways to capture success. Increasing intervention success rates requires new digital tools for streamlined modeling, standardized method selection workflows, and data collection and insights to analyze and improve existing approaches. A novel cloud digital platform is introduced to drive the intervention method selection workflow. Starting from a candidate well, the software enables modeling of the outcome for different scenarios. A standardized canvas enables capturing the key operations, cost, and duration and comparing multiple alternatives. The data are stored in the cloud and can be easily accessed and updated by the involved parties in a collaborative manner. New information can be used to reevaluate decisions, increase efficiency, and ensure the right intervention method selection. The data from execution is captured in a database and can be used to improve future operations. Managing water production is crucial for maintaining the efficiency and profitability of wells. When an increase in water cut is observed, it is essential to identify and shut off the zones producing excess water. This is typically done using a production logging tool, which helps determine the contribution of each zone. Additionally, nodal analysis is employed to predict the outcomes of shutting off water, such as increased oil production and reduced water production. This approach requires collaboration of multiple types of expertise and software. The cloud intervention planning platform has been used in wells in the Norwegian Continental Shelf requiring interventions and water shutoffs. The cloud intervention planning platform was used to create well models and capture alternative operational plans in the canvas, enabling a quick comparison of alternatives. Once surveillance was conducted, the interpreted data were input into the cloud intervention planning platform and used to quickly recalculate the outcomes of alternatives. Multiple what-if visual flow profiles were generated and compared to choose the water shutoff intervention for optimum recovery. Post-intervention, well flow rates were compared with predicted rates, i.e., digital vs. actual intervention. The cloud intervention planning platform represents the first tool for a workflow and modeling combined approach to intervention method selection. What-if intervention scenarios help to create a digital flow profile that enables informed decision making, comparing prediction against actual, and using it to capture success. Its application is not limited to production, and the addition of new models can expand the workflow to additional intervention disciplines.