Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 175916, “The ‘Well-Factory’ Approach to Developing Unconventionals: A Case Study From the Permian Basin Wolfcamp Play,” by Jarrad Rexilius, Chevron, prepared for the 2015 SPE/CSUR Unconventional Resources Conference, Calgary, 20–22 October. The paper has not been peer reviewed. In order for operators to grow production and maintain profit margins in unconventional-resource plays, a “well-factory” or “manufacturing-based” style of development is often used. This paper will analyze differing well-factory approaches to unconventional assets, with examples from the Wolfcamp unconventional oil play in the Permian Basin. An emphasis is placed on using a well-factory model that enables flexibility for project-execution teams to optimize, while maintaining the efficiency and execution speeds that a classical factory model provides. Introduction With the relatively recent boom in unconventional-resource plays, the concept of manufacturing has been widely proposed and applied to the upstream industry. Many companies across the globe have adopted well-factory models and a manufacturing-based approach in developing large-acreage positions in unconventional plays. A common theme across industry literature is the claim that a manufacturing approach to unconventional-resource development leads to greater efficiencies with regard to drill days and well costs. These improvements are largely attributed to supply-chain and contract optimization, logistical efficiencies, and materials management. A common theme in literature devoted to the well-factory approach, however, is the lack of discussion concerning well recoveries and maximizing reserves. By focusing only on costs and cycle times, decisions are quickly made that can affect the ultimate recovery of wells and therefore diminish overall economic return from the wells. A one-size-fits-all approach with standardized designs and strict work processes can lead to suboptimal economic development plans and erode the value of oil projects. Flexible and Adaptable Factory Model To date, the development philosophy of many operators in the unconventional space has been to drill as many identical wells as possible as quickly as possible. These metrics of speed and cost have had the desired result of enabling production growth for the development area. However, operators are noting that such a method often results in many underperforming wells and more surprises during the execution phase. Practitioners are finding that subsurface environments can change dramatically over hundreds of feet, and that simply drilling more of these wells in the same fashion will lead to value erosion and production inefficiencies. Another misconception with these resource plays is the notion that gathering of data—such as openhole logs—is not important. It is important that a factory model provide sufficient flexibility to enable operators to modify plans to prevent poor economic performance of investments. There is a balance to be made between use of this adaptive and flexible approach and maintenance of the efficiencies and economies of scale provided from a well-factory style of development.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".