Optimizing Shale Development: Another Approach By Means of Multilateral Wellbores
Bibliographic record
Abstract
Abstract Multilateral (ML) wells have been synonymous with the means to improve recovery from conventional reservoirs by adding drainage. Throughout the years, there have been limited ML well applications in various US unconventional plays. Widespread use has likely been curtailed by the challenging commodity price environment that has forced operators to drill and complete wells faster to lower recovery costs and provide production to the market quicker. This paper discusses the use of ML technology in unconventional programs to optimize shale development. Thousands of ML wells have been drilled and completed to date in many conventional applications worldwide. Applications range from benign projects to more complex programs that include deepwater, subsea, and extended-reach projects. Many unconventional heavy oil applications have been developed in various regions using ML technology during the last 20 years. The use of ML technology in many of these applications created efficiency in the operator's development plans because fewer wells were necessary to drain reservoirs more effectively. This helped save drilling and completion, production and surface equipment, and all other related infrastructure costs. Early this decade, ML technology also began to make inroads in the shale oil and gas marketplace. ML technology has been used to develop a limited number of unconventional shale and tight oil and gas wells in both Canada and the contiguous US. Some of the published data addressed the operational issues, surface logistics, and general efficiencies of many of these previous ML multistage projects. This paper presents a case study that examines in additional detail the drilling and completion of two single horizontal wells vs. a single ML well in an unconventional play. This paper discusses the potential benefits of ML applications and modifications to consider during the drilling and completion of shale and/or tight oil and gas wells. Objective viewpoints are also presented to generate further discussion.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".