No abstract

PDO is transforming its field development planning by adopting digital technologies and Artificial Intelligence (AI) to improve organizational efficiency and maximize business value through swift quality decisions and an evergreen forecast. In this context, the company has approached a number of third parties to bring in solutions in this domain. In 2021 one such collaboration with 3rd party contractor to test a novel solution involving data driven AI based dynamic simulator in a mature brown fiend setting. The objective was to test the tool, the efficacy and efficiency of the process, robustness and ease of use and its utility in current setting [1]. Existing dynamic modelling workflows with conventional simulators are extremely time consuming to update and upgrade in a mature brownfield setting. These conventional and lengthy iterative process of working might leave value on the table. It is time consuming to update history match with all the extra inputs and forecast; and optimizing the development with all the input parameters within a short timeframe is always a challenge. The process employed in this approach was based on deep learning artificial neural networks (ANN) coupled with numerical simulators and along other static model inputs. The reservoir static and the flow dynamics were used as feature parameters to train the ANN, while the historical field production was used as the target parameters. The ANN training exercise identified the most contributed static and dynamic parameters to the historical production; therefore, these main parameters were given a higher weight in production forecasting and reservoir management. This AI-simulation method was expected to be faster, data driven and allow a faster testing of multiple development strategies in short time. This paper outlines the experience of an AI-assisted numerical simulation approach to unlock the potential of brown oil fields in south Oman by reducing the time spent on modelling and base case anchoring. It also enables evergreen forecasting by integrating AI techniques with numerical simulation. The AI-simulation was tried in a brown field with an existing FDP generated using conventional simulation tool where >50% of the FDP propose wells have been drilled. The outcomes from the AI-simulation result were compared with conventional simulation and with Actual field performance. Optimization was also conducted to locate the sweet spots for future drilling and WRFM opportunities. This optimized workflow has the potential to enable step change improvements in time and value for brownfield development and optimization for future developments.

Real time tubing inspection during hoist interventions was implemented in South Oman in late 2007. It utilizes the principle of electromagnetic pipe inspection. The technique is similar to conventional yard based electro magnetic tubing inspection, the difference being that yard inspection is performed on the ground whereas real time tubing inspection is performed on the well. It provides scientific assessment of tubing condition in real time.

In recent years, with the steep drop and increased volatility in oil price, there is an urgency for making our field (re-development) plans more dynamic and efficient with faster payback and with particular emphasis on robustness against uncertainties. This paper describes a root cause analysis and a methodology to achieve up to ~30% improvement in field development planning project cycle and developing a better-integrated reservoir understanding. A comprehensive integrated analysis of available data is a key success criterion for robust decision-making. A detailed value stream mapping and a timeline analysis for data analysis in the hydrocarbon maturation process revealed that our process cycle efficiency is only 16% with a significant room for improvement. Any improvement can be directly translated to man-hour cost saving and acceleration of oil delivery. Effective use of technology and digitalization for knowledge management, standardized ways of working and easy access to historical data, analysis and diagnostics were identified as key focus areas to improve delivery. An innovative process and web based digital platform, iResDAT, is developed for accelerating data analysis. It mines from volumes of petro-technical databases and translates data into standardized diagnostics using latest data analytics and visualization technologies. It has already reduced dramatically the time to mine critical subsurface data and prepare required integrated diagnostics that are auditable and can be re-created in a few seconds. Based on the early pilot studies the cycle time reduction in the data analysis phase is close to 30% with improved quality and standardization of the integrated analysis. It has already transformed the ways of working where the subsurface discussion can happen across disciplines using a single platform that enforces early integration for reservoir understanding and associated uncertainty characterization. It is a web-based platform where the diagnostic dashboards are crowd sourced; sustained and enhanced by the business to ensure the relevance and sustainability with the Corporate Data Management and IT functions. It is a building block towards quality controlled and auditable data analysis and interpreted dataset, which may form the backbone for any advanced analytics in future to enable digitally enabled hydrocarbon maturation.