Bibliographic record
Abstract
Technology Focus The wave of technology change in tight-reservoir exploration and production continues to gather momentum, with completion strategies and hydraulic-fracture stimulations customized to reservoir heterogeneity and quality as the central drivers for improvement. Another noteworthy driver for technology change is environmental-footprint reduction. Proppant-placement technologies now provide production enhancement with reduction in sand and water consumption. Other technologies are enabling the reuse of produced and flowback waters. Tubular, materials, and fluids technologies are evolving to meet the challenges encountered in tight-reservoir drilling, especially for high-temperature or high-pressure applications in “soft” rock. Completion products and practices, such as casing selection, wellhead selection, and numbers of perforations and their placement with regard to the planned fracturing stages, are evolving to enhance production from multistage-fractured, long horizontal wells while reducing the associated costs. Thanks to basinwide reservoir and microseismic data, hydraulic-fracture modeling is evolving to rationalize the apparent contradiction between anecdotal evidence and established theories for (1) fracture initiation, growth, and closure; (2) fracturing-fluid functional requirements; and (3) proppant selection, transport, and placement. At the same time, with reservoir-specific formation and production data, there continues to be growth in the understanding of production mechanisms and their relationship to the various formation types and understanding of geomechanical responses that result from hydraulic fracturing. Examples of such understanding are changes in permeability influenced by pore volume, near-wellbore choking as a function of rock properties, and slip/shear response as a function of rock properties. Plenty of progress remains to be made in linking together the production mechanisms, geomechanical responses, and hydraulic-fracturing models into an effective knowledge base. Opportunities still exist in then using this knowledge base to create workflows for better completion strategies and hydraulic-fracture stimulations. This month’s feature presents papers and a reading list that reflect several of these observations. Recommended additional reading at OnePetro: www.onepetro.org. SPE 148940 Stimulation's Influence on Production in the Haynesville Shale: A Playwide Examination of Fracture-Treatment Variables That Show Effect on Production by Neil Modeland, Halliburton, et al. (See JPT, March 2012, Page 62.) SPE 155640 Gas Flow Tightly Coupled to Elastoplastic Geomechanics for Tight and Shale Gas Reservoirs: Material Failure and Enhanced Permeability by Jihoon Kim, Lawrence Berkeley National Laboratory, et al. SPE 147462 Improving Fracture-Initiation Predictions on Arbitrarily Oriented Wells in Anisotropic Shales by Romain Prioul, Schlumberger-Doll Research, et al. SPE 155756 A Pore-Scale Gas-Flow Model for Shale-Gas Reservoir by Vivek Swami, University of Calgary, 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.240 | 0.072 |
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".