Expectations from data analytics in the upstream sector continue to evolve. Although the number and diversity of applications continue to increase, the adoption at the assetwide level faces well-known barriers and challenges. Massive data collected from field instrumentation remain unexploited to the full potential because of misalignment between people, processes, and technologies.
To begin with, a lack of consistent data-integration infrastructure (e.g., between secure field networks and corporate data repositories) makes accessing and using data in real time difficult. It is disappointing that small, missing, or broken links can interrupt the whole data flow. For example, data from a fully instrumented field may not be leveraged in the data-analytics domain because of a lack of an integrated gateway between two networks within the same company.
Furthermore, although the number of data-analytics applications continues to grow, data-assimilation algorithms are still gaining momentum in being accepted as business as usual. A disconnect exists between the available operational challenges and the applications being developed, in some cases because of data completeness and availability and in other cases because of a mismatch between the technology-driven organizations and the asset owners, operators, and decision makers.
Finally, adopting such business processes requires dedicated people with the expertise to exploit the data, with business-intelligence skills and experience with data analytics or automated-work-flow building. The competency gap does not appear to be reduced despite the avalanche of data-analytics self-training programs; it remains a highly scientific mathematical approach to solve problems, accessible to few.
The good news is that success stories are everywhere. Traditionally unsolved upstream challenges have the potential to be addressed by self-clustering and dimensionality-reduction techniques that uncover hidden patterns and trends in data from drilling, production, completion, seismic, and logs.
As an example of success, engineers can gather data from thousands of wells and automatically (without previous knowledge or target outputs) group these wells into a small subset that is similar or related through different attributes, such as rates, reservoir quality, contact area, completion parameters, or location. These applications clearly have benefited operators and service companies to focus quickly on value-added decisions, which was not possible previously unless senior experience was considered.
Assets and operators should strive to understand the value that predictive analytics can provide to their bottom line. I invite you to review the selection of key papers in this feature.
In addition, I would like to invite you to try the new SPE advance search engine (https://search.spe.org), which uses advanced analytics and artificial-intelligence techniques to offer a better experience for finding and analyzing information on SPE.org, PetroWiki, and OnePetro.
This Month's Technical Papers
Face-Detection Algorithm Handles Big Data To Help Identify Candidates for Restimulation
Simulation Algorithm Benefits by Connecting Geostatistics With Unsupervised Learning
New Method for Predicting Production Boosts Accuracy for Carbonate Reservoirs
Recommended Additional Reading
SPE 190812 Status of Data-Driven Methods and Their Application in the Oil and Gas Industry by Karthik Balaji, University of North Dakota, et al.
SPE 187030 Logging Facies Classification and Permeability Evaluation: Multiresolution Graph-Based Clustering by Xinlei Shi, China National Offshore Oil Corporation, et al.
OTC 28002 Multiscale Ensemble-Based Data Assimilation for Reservoir Characterization and Production Forecast: Application to a Real Field by Alexandre de Lima, CGG, et al.
Luigi Saputelli, SPE, is a senior reservoir engineering adviser with ADNOC. During the past 25 years, he has held various 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 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 fields, reservoir management, reservoir engineering, real-time optimization, and production operations. He holds a BS degree in electronic engineering from Simón 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.
