Review of the Leak-off Tests with a Focus on Automation and Digitalization - Bakhshi, Elham; Elahifar, Behzad; Shahrabadi, Abbas; Golsanami , Naser; Khajenaeini, Reza (International Journal of Petroleum Technology, 2022)
The drilling and research communities are leading the way toward more digitally-controlled operations to ensure that the drilling process takes place as safely and gently as possible with the lowest possible carbon footprint. Today’s cutting-edge operations are run on large high-performance drilling installations where operations are largely run remotely from the driller’s operating station. Digitalization of the drilling process is the goal for performing drilling operations remotely from onshore. Leak-off test (LOT) or extended leak-off test (XLOT) plays a critical role in the petroleum industry. Therefore, recognizing all affecting parameters on LOT/XLOT and Formation integrity test (FIT) performance is vital. Because, in some cases, it is not possible to fully understand what happened during the test, having a deep insight into the LOT procedure is very important. One of the current study’s main objectives is to thoroughly explain all stages of these tests and assemble all the significant parameters. Thus, many scientific papers on these tests were deeply reviewed and were classified into four main groups focusing on the application of LOT/XLOT (i) in stress estimation and geomechanical studies, (ii) concerning hydraulic fracturing, (iii) concerning wellbore stability, and (iv) numerical modeling, and then, the corresponding discussions were conducted. It was found that in-situ stress estimation is the most common application of the leak-off test. Moreover, considering the importance of LOT and the desire to digitize operations in the oil and gas industry, it was found that the automatic LOT/XLOT is a fully required approach. The primary purpose of this study, which is hence considered its main contribution, is to prepare a LOT flowchart that would set off the further code development tasks of the field. The fundamental code of the present study was written and checked using a real dataset in a Python environment. The results were satisfying and indicated a successful start, which lays a foundation for future automated LOT/XLOT tests.