Technology Focus: Reservoir Performance and Monitoring (September 2015)
Bibliographic record
Abstract
Technology Focus Recent months have been very challenging for the oil and gas industry. When the previous Reservoir Performance and Monitoring feature appeared in JPT in September 2014, Brent was trading at approximately USD 105/bbl. At the end of June 2015, when this statement was written, its price was 41% lower, at just above USD 62/bbl. In addition, the world rig counts reported by Baker Hughes in September 2014 and June 2015 were 3,659 and 2,152, respectively, a decrease of approximately 41% (rig count is a trailing indicator of oil price). The international and North America rigcount decreases between September 2014 and June 2015 were approximately 12 and 57%, respectively. These steep decreases, mostly the direct consequence of a global imbalance between demand and supply, are warnings that our conventional innovation schemes need to be recalibrated. Even if the rig count has been dropping, the rig efficiency continues to improve. From multiwell pads to advanced drilling technologies, innovation is helping keep current production high. If current low prices persist, further innovation may improve the alignment between short-term production and long-term recovery, while lowering overall production costs. Maximizing short-term production and optimizing longterm recovery may provide key opportunities for cost savings during the next downturn of major proportions. This could be achieved through the industry’s ability to innovatively acquire and interpret data for optimizing the reservoir performance. The industry is continuously looking at new monitoring devices and techniques, and there are huge opportunities for monitoring fieldwide data with the ultimate goal of understanding the reservoir better and predicting its short- and long-term production more accurately. The current downturn may be a great opportunity for the big-data revolution from other industries to be extended to the oil and gas industry in general and to reservoir performance in particular. As a result of the industry’s current efforts to improve reservoir performance and reduce production costs, many great papers have been presented at recent SPE conferences and meetings. From the more than 100 papers reviewed for this feature, approximately half of them present case histories, field-data interpretation, and work flows, and the other half present theoretical and laboratory results. The papers summarized in this feature and recommended as additional reading are excellent samples of this distribution. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 170619 Wireless Inflow Monitoring in a Subsea Field Development: A Case Study From the Hyme Field, Offshore Mid-Norway by Svein Mjaaland, Statoil, et al. IPTC 18115 Three-Dimensional Visualization of Solvent Chamber Growth in Solvent-Injection Processes: An Experimental Approach by F. Fang, University of Alberta, et al. SPE 171932 Linking Diagenesis, NMR, and Dynamic Data for Accurate Flow Characterization of Heterogeneous Carbonate Reservoir by Umer Farooq, Abu Dhabi Company for Onshore Oil Operations, et al. SPE 172929 Production Forecast, Analysis, and Simulation of Eagle Ford Shale Oil Wells by Basel Alotaibi, Texas A&M University, et al.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".