Petroleum Data Analytics

While many other industries have experienced tremendous benefits over the last few decades, adoption of data-­driven analytics is still young in the oil and gas sectors. Benefits captured across industries involve improving the quality of decisions, improving planning and forecasting, lowering costs, and driving operational-efficiency improvements. However, many challenges for full adoption exist in our industry. In addition to the outdated data-management challenges, key gaps exist in the understanding of basic principles concerning how and when to use different data-analytics tools. 

Data-analytics benefits are being demonstrated through the efficient exploitation of data sources to derive insights and support making decisions. An exponential increase in the number of applications in recent years has been observed for enhancing data quality during/after acquisition by automatically removing noise and outliers; better assimilating new and high-frequency data into physics-based models; optimizing calendar-based inspections for preventive-maintenance tasks; increasing equipment availability of well, surface, and drilling systems; optimizing reservoir recovery on the basis of injector-to-producer allocation factors; and many others.

Machine learning is a collection of techniques, both supervised and unsupervised, that gives computers the ability to learn and adapt without being explicitly programmed. This ability to learn provides capabilities for describing past and current operating conditions, predicting, and prescribing.

Supervised learning includes regression and classification methods in which a relationship is established between the input and a known output. Unsupervised learning includes clustering, which addresses problems with no prior knowledge on the output, automatically grouping a large number of data variables into a smaller variable set.

Most data-driven projects may follow a similar approach during implementation. In the majority of these, large efforts are made in collecting and preparing the data, which typically reside in scatter sources and exist in unstructured formats. Once data are placed in proper tabular forms and relationships are established, then data are ready for analysis, which may include exploratory visualizations, model order or dimensionality reduction, clustering, regression, classification, pattern recognition, cross validation, model validation, prediction, and optimization. Insights and syntheses are derived along the analysis process.

Text mining and natural language processing (NLP) allows the possibility of efficiently extracting valuable information from text documents and reports. These methods enable an unexploited yet powerful source of insights about operational transactions (e.g., recommendations, success/failure) that are captured in unstructured text. In the drilling area, NLP has been used successfully to describe and predict nonproductive-time and invisible-lost-time causes from a massive amount of unstructured data collected from the drilling-operation reports. Major contributions will also occur in reservoir management and production optimization.

This Month's Technical Papers

Technique Blends Dimensionless Numbers and Data Mining To Predict Recovery Factors

Proxy-Based Metamodeling Optimization of Gas-Assisted-Gravity-Drainage Process

Natural-Language-Processing Techniques for Oil and Gas Drilling Data

Recommended Additional Reading

SPE 181015 Natural Language Processing Techniques on Oil and Gas Drilling Data by M. Antoniak, Maana, et al.

OTC 27577 Assessment of Data-Driven Machine-Learning Techniques for Machinery Prognostics of Offshore Assets by Ping Lu, American Bureau of Shipping, et al.

SPE 181037 Big Data Analytics for Prognostic Foresight by Moritz von Plate, Cassantec

SPE 185695 A Novel Adaptive Nonlinear-Regression Method To Predict Shale Oil Well Performance on the Basis of Well Completions and Fracturing Data by Amol Bakshi, Chevron, et al.

Luigi Saputelli, SPE, is a senior reservoir engineering adviser with ADNOC. During the past 25 years, he has held positions as reservoir engineer, drilling engineer, and production engineer. Saputelli previously worked for 3 years with Hess Corporation, for 5 years with Halliburton, and for 11 years with Petróleos de Venezuela. He is a founding member of the SPE Petroleum Data-Driven Analytics Technical Section and the recipient of the 2015 SPE International Production and Operations Award. Saputelli has authored or coauthored more than 70 technical publications in the areas of digital oil field, reservoir management, reservoir engineering, real-time optimization, and production operations. He holds a BS degree in electronic engineering from Simon Bolívar University, an MS degree in petroleum engineering from Imperial College London, and a PhD degree in chemical engineering from the University of Houston. Saputelli serves on the JPT Editorial Committee, the SPE Production and Operations Advisory Committee, and the Reservoir Description and Dynamics Digital Oil Field subcommittee. He has served as a reviewer for SPE Journal and SPE Reservoir Evaluation & Engineering and as an associate editor for SPE Economics & Management. Saputelli also serves as managing partner at Frontender, a petroleum-engineering-services firm based in Houston. He can be reached at lsaputelli@frontender.com.

Petroleum Data Analytics

Luigi Saputelli, SPE, Senior Reservoir Engineering Adviser, ADNOC, and Frontender

01 October 2017

Volume: 69 | Issue: 10

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