AUTO-NAVIGATION ON PRESSURE AND SAMPLING LOCATION IN WIRELIN
Measuring formation pressure and collecting representative samples are the essential tasks of formation testing operations. Where, when, and how to measure pressure or collect samples are the critical questions that must be addressed in order to complete any job successfully. Formation testing data has a crucial role in reserve estimation, especially at the stage of field exploration and appraisal, but can be time consuming and expensive. The optimum location has a major impact on both the time spent performing and the success of pressure testing and sampling. Success and optimization of rig time paradoxically require careful and extensive but also quick prejob planning. The current practice of finding optimum locations for testing heavily relies on expert knowledge. With nearly complete digitization of data collection, the oil industry is now dealing with massive data flow, giving rise to the question of its application and the necessity to collect. Some data may be so-called “dark data,” of which a very tiny portion is used for decision making. For instance, a variety of petrophysical logs may be collected in a single well to provide measures of formation properties. The logs may include conventional gamma ray, neutron-density, caliper, resistivity, or more advanced conventional logs, such as high-resolution image logs, acoustic, or nuclear magnetic resonance (NMR). These data can be integrated to help decide where to pressure test and sample; however, this effort is nearly exclusively driven by experts and is manpower intensive. In this paper, we present a workflow to gather, process, and analyze conventional log data in order to optimize formation testing operations. The data is from an enormous geographic distribution of wells. Tremendous effort has been performed in the extract, transform, and loading (ETL) of the data into a usable format. The data contains multimillion to multibillions of rows, thereby creating technology challenges in terms of reading, processing, and analyzing in a timely manner for prejob planning. We address the technological challenges by deploying cutting-edge data technology to solve this problem. Upon completion of the workflow, we have been able to build a scalable petrophysical log data platform, which can be easily used for machine learning and application deployment. This type of database is an invaluable asset, especially in places where there is a need for knowledge of analogous wells. Exploratory data analysis on mobility and some key influencing features on pressure test and sampling quality on worldwide data is performed and presented. We further show how these data are integrated and analyzed in order to automate selection locations for which to formation test.
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Author(s):
Mehdi Alipour K, Bin Dai, Jimmy Price, Christopher Michael Jones, Darren Gascooke, Anthony VanZuilek
Company(s):
Halliburton
Year:
2021