Machine Learning Based Automated Pressure Testing in Carbonate Formations of UAE

Wireline formation pressure tests (pretests), are critical in reservoir characterization during exploration stage, providing reservoir pressure and mobility. We have demonstrated the application of a novel and fully automated digital pretest workflow in onshore, UAE. The use of intelligent real-time pretest classification combined with automated setting and retract of pretest probes led to significant reduction in overall test durations with increased consistency in results. These advancements allowed the reservoir engineers and asset managers to focus on interpretation of results, enhancing reservoir characterization and reducing operational risks This novel workflow is enabled by four automation stages combined and executed over a single command from a new customized acquisition system at wellsite: Automated setting of pretest probes, automated real time pretest classification (RTPC), automated pressure and mobility estimation and automated retract of probes. The new acquisition system is mounted with both logging tool electronic control algorithm and pretest interpretation algorithms, hence bringing the pretest interpretation to the wellsite minimizing the time loss due to delay in signal transmission from wellsite to the office. The first stage includes automated setting of probes in less than 30 seconds compared to conventional duration of 90 seconds. Next stage is automated pretest classification, where K-nearest neighbor (KNN) & Light grading boosting machine (LGBM) based AI/ML algorithms are used to classify the pretest type as valid test or invalid test. For valid tests, pressure stabilization is monitored over 10 second windows, and mobility is auto computed. For an invalid test, the fourth automation stage is triggered, leading to retracting probes in 10 seconds with 95% accuracy. This process reduced manual interventions from 25 clicks to 5 per station, ensuring consistency in operations across multiple depths. A total of 93 pretests were taken using a pre-planned automation algorithm executed over single command at each depth. The improvement in pretest efficiency and consistency was measured in time saving and data quality. Around 80% reduction in time spent on probe setting and retract was observed. The average pretest station time reduced from 13 minutes to less than 8 minutes, achieving a 38% overall time reduction. Tight tests and valid tests saw a 50%- and 40%-time reduction, leading to an overall time saving of over 7 hours during the pretest logging operation. Faster testing in openhole conditions also reduced wireline sticking risks. We showcased the application of AI & ML automation algorithms, tested and trained over 890 and 153 datasets respectively, and deployed on more than 20 real time pretest operations across the globe, before being used in this pretest operation. The real time pretest classification (RTPC) brought speed and consistency by reducing the coefficient of human error in such long pretest operations, leading to an efficient and standardized way of pressure testing.