Auto-Navigation on Pressure and Sampling Location in Wireline and LWD: Big Data Challenges and Solutions

Alipour K, Mehdi (Halliburton) | Dai, Bin (Halliburton) | Price, Jimmy (Halliburton) | Jones, Christopher Michael (Halliburton) | Gascooke, Darren (Halliburton) | VanZuilekom, Anthony (Halliburton) | Tahani, Hoda (Halliburton) | Ahmed, Fahad (Halliburton) | Hamza, Farrukh (Halliburton) | Sungkorn, Radompon (Halliburton) | Rekully, Cameron (Halliburton)


Abstract 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 critical questions which 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. 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 requires careful and extensive but also quick pre-job planning. The current practice of finding optimum locations for testing heavily rely 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 tools such as high-resolution image logs, acoustic, or 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 to extract, transform and load (ETL) the data into a usable format. Stored files contains multi-million to multi-billions rows of data thereby creating technology challenges in terms of reading, processing and analyzing in a timely manner for pre-job 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 interpretation log platform which can be easily utilized for machine learning and application deployment. This type of data base is invaluable asset especially in places where there is a need for knowledge of analogous wells. Exploratory data analysis on worldwide data on mobility and some key influencing features on pressure test and sampling quality, is performed and presented. We further show how this data is integrated and analyzed in order to automate selection of locations for which to formation test.

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