Technology Focus: Petroleum Data Analytics (October 2018)
- Luigi Saputelli (ADNOC / Frontender Corporation)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- October 2018
- Document Type
- Journal Paper
- 61 - 61
- 2018. Copyright is retained by the author. This document is distributed by SPE with the permission of the author. Contact the author for permission to use material from this document.
- 4 in the last 30 days
- 197 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||Free|
|SPE Non-Member Price:||USD 4.00|
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.
Recommended additional reading at OnePetro: www.onepetro.org.
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.
|File Size||111 KB||Number of Pages||1|