Bibliographic record
Abstract
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 146998, ’Improving Horizontal Completions in Heterogeneous Tight Shales,’ by Roberto Suarez-Rivera, SPE, Chaitanya Deenadayalu, Maxim Chertov, SPE, Ricardo Novalo Hartanto, SPE, and Patrick Gathogo, Schlumberger, and Rahul Kunjir, University of Utah, prepared for the 2011 Canadian Unconventional Resources Conference, Calgary, 15-17 November. The paper has not been peer reviewed. Production from nanodarcy-range-permeability shale formations requires extensive hydraulic fracturing, large volumes of water, and closely spaced wells. Comparing calculations of the possible fracture-surface area created during treatments to production results indicates that a large portion of that surface area is ineffective for production, resulting in ineffective use of resources. A fundamental understanding is required to improve the efficiency of horizontal completions in producing shales. The objective of this study was to improve completion design and horizontal-well-completion efficiency. Introduction When considering tight-shale-formation characterization and completion design, one should evaluate the formation characteristics conducive to economic production: reservoir quality (RQ), representing the multiple properties defining reservoir potential, and completion quality (CQ), representing the multiple properties defining the potential for creating and sustaining a large surface area in contact with the reservoir. RQ and CQ properties vary in the near-wellbore and far-wellbore regions. For CQ, the far-wellbore region represents the region of contact between the created fracture and the reservoir. Well production depends on this surface area being in contact with good RQ, and depends on conditions of containment, fracturability, rock/fluid interactions, and loss of surface area and fracture conductivity during production. The near-wellbore region represents the choking point between the created surface area and the wellbore. The goal is to maximize connectivity between the fracture system and the wellbore. This goal is attainable by minimizing near-fracture tortuosity, maximizing fracture width, reducing breakdown pressures, and limiting the risk of solids production. The result is a nonsubjective and consistent method that provides a means for understanding variability in fracture performance along wellbores (e.g., inferred from microseismic monitoring, trace analysis, and stage-by-stage flow measurements) and for selecting perforation stages on the basis of measured or log-inferred rock properties. The method also provides a means for monitoring consistency between the predicted values and measured results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".