Optimizing Wireline Formation Testing in Oil Wells: A Data Science approach

• Marcelo Abbehusen Petrobras https://orcid.org/0009-0007-8356-6710
• Rodolfo Rodrigues Petrobras https://orcid.org/0009-0001-1512-3367
• Lucas Oliveira Petrobras https://orcid.org/0000-0002-1404-0703
• Tatiana Escovedo PUC-Rio https://orcid.org/0000-0002-7130-4330
• Marcos Kalinowski PUC-Rio https://orcid.org/0000-0003-1445-3425

Resumo

Context: Wireline Formation Testers are common operations in well testing and evaluation, as they provide key information for exploration and production activities, such as reservoir pressure and permeability. The operation is conducted by following a line graph, where the X and Y axes are time and pressure, respectively. Problem: The decision on whether to end or not the operation is based on this graph. Unfortunately, there is no consensus on the ideal method to conduct these operations, and that is precisely the objective of this work. Solution: Using mathematical modeling, optimization and data science techniques, this work aims to increase the efficiency of wireline formation testing, defining objective metrics for operations conductions, in order to reduce time and guarantee data quality.IS Theory: This work is associated with the Theory of Computational Learning, which aims to understand the fundamental principles of learning and design better-automated methods. Method: The research has a prescriptive character, following machine learning model building best-practices and using a quantitative approach in analyzing results. Summary of Results: The results obtained show that there is a clear potential for reducing operating time and, therefore, costs, if the proposed methodology is used in routine operations. Contributions and Impact in the IS area: This article shows that, through mathematical modeling and the application of data science techniques, it is possible to significantly reduce the time of Wireline Formation Test operations, without any relevant loss of information, which can be a significant gain for oil and gas companies.

Palavras-chave: Formation test, wireline, well logging, formation pressure, optimization, machine learning

Referências

Betancourt, S. S., “Some Aspects of Deep Formation Testing”, M.S. thesis, University of Texas, Austin, May. 2012. [Online]. Available: https://repositories.lib.utexas.edu/handle/2152/ETD-UT-2012-05-5232

Johnson, Ashley, Mäkinen, Anna, Fahim, Syed, and Yezid Arevalo. Quantification of Our Carbon Footprint While Drilling. Paper presented at the IADC/SPE International Drilling Conference and Exhibition, Galveston, Texas, USA, March 2022. doi: https://doi.org/10.2118/208737-MS.

Proett, Mark A., Waid, Margaret C., Heinze, Jim, and Mark W. Franki. Low Permeability Interpretation Using A New Wireline Formation Tester Tight Zone Pressure Transient Analysis. Paper presented at the SPWLA 35th Annual Logging Symposium, Tulsa, Oklahoma, June 1994.

Proett, Mark A. Real Time Pressure Transient Analysis Methods Applied to Wireline Formation Test Data. Paper presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 1994. doi: https://doi.org/10.2118/28449-MS

Proett, Mark, Musharfi, Nedhal, Meridji, Yacine, Gill, Harmohan, and Sami Eyuboglu. Objectively Quantifying Wireline and LWD Pressure Test Quality. Paper presented at the SPWLA 55th Annual Logging Symposium, Abu Dhabi, United Arab Emirates, May 2014.

Newville, Matthew; Stensitzki, Till; Allen, Daniel B.; Ingargiola, Antonino. LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python. 2014.

Proett, Mark A., and Wilson C. Chin. Supercharge Pressure Compensation With New Wireline Formation Testing Method. Paper presented at the SPWLA 37th Annual Logging Symposium, New Orleans, Louisiana, June 1996.

Ali, Moez. “Pycaret: An open source, low-code machine learning library in Python”. Available: https://www.pycaret.org, April 2020.

Allen, Marcos. Measuring of Carbon Footprint in Offshore Drilling. Paper presented at the IADC/SPE International Drilling Conference and Exhibition, Galveston, Texas, USA, March 2022. doi: https://doi.org/10.2118/208683-MS.