Data Science for Water Cut Analysis System by Utilizing ESP Sensor Data, Conceptual Model, and Its Proposed Solution

Objective/scope It has been a challenge to analyze and estimate reliable water cut. The current well test data is not sufficient to satisfy the required information for prediction of the rate and water cut behaviors. Only on wells having stable and good behaviors, water cut levels can be estimated appropriately. The wells have Electrical Submersible Pump (ESP) sensor reading and data acquisition recorded in real-time help to fill this gap. The data are stored and available in KOC data repositories, such as Corporate Database, Well Surveillance Management System (WSMS), and Artificial Lift Management System (ALMS) Engineers spend this effort in spreadsheets and working with multiple data repositories. It is fit for data analysis by combining the data into a simple data set and presentation. Nevertheless, spreadsheets do not address a number of important tasks in a typical analyst's pipeline, and their design frequently complicates the analyses. It may take hours for single well analysis and days for multi-wells analysis and could be too late to plan and take preventive actions. Concerning the above situation, collaboration has been performed between NFD-North Kuwait and Information Management Team. In this first phase, this initiative is to design a conceptual integrated preventive system, which provide easy and quick tool to compute water cut estimation from well tests and downhole sensors data by using data science approach. Method, procedure, process There are 5 steps were applied in this initial work. It was included but not limited to user interview, exercise and performed data dissemination. It included gather full knowledge and defining the goal. Mapping pain points to solution also conducted to identify the technical challenge and find ways to overcome them. In the end of this stage, data and process review was conducted and applied for a given simple example to understand the requirements, demonstrate technical functionality and verify technical feasibility. Then conceptual design was built based on the requirements, features, and solutions gathered. Integrated system solution was recommended to include intermediate layer for integration, data retrieval, running calculation-heavy process in background, model optimization, visual analytics, decision-making, and automation. A roadmap with complete planning of different phases is then provided to achieve the objective. Results, observations, conclusions Process, functionalities, requirements, and finding have been examined and elaborated. The conceptual design has proved and assured the utilization of ESP sensor data in helping to estimate continuous well water cut's behavior. Further, the next implementation phase of data science expects an increase of confidence level of the results into higher degree. The design is promising to achieve the requirement to provide seamless, scalable, and easy to deploy automation capability tools for data analytic workflow with several major business benefits arising. Proposed solution includes combination of technologies, implementation services, and project management. The proposed technology components are distributed into 3 layers, source data, data science layer, and visual analytics layer. Furthermore, a roadmap of the project along with the recommendation for each phase has also been included. Novel/additive information Data Science for Exploration and Production is new area in which research and development will be required. Data science driven approach and application of digital transformation enables an integrated preventive system providing solution to compute water cut estimation from well tests and downhole sensors data. In the next larger scale of implementation, this system is expected to provide automated workflow supporting engineers in their daily tasks leveraging Data to Decision (D2D) approach. Machine learning is a data analytics technique that teaches computers to do what comes naturally to human, which is learn from experience. Machine learning algorithm use computational methods to learn information from the data without relying on predetermined equation as a model. Adding artificial intelligence and machine learning capability into the process requires knowledge on input data, the impact of data on the output, understanding of machine learning algorithm and building the model required to meet the expected output.