Analyzing Offset Wells for Benchmarking UC Gas Well's Technical Limit in Field Development De-Risking Strategy: A Digital Solution
Bibliographic record
Abstract
Abstract This technical abstract delves into optimizing well delivery time in unconventional drilling projects by leveraging digital solutions, focusing on offset well data analysis, invisible lost time (ILT), and well time benchmarking. Emphasizing the complexities of limited well counts within de-risking strategies, the integration of digital technology has revolutionized conventional drilling practices, improving operational efficiency and productivity in well drilling and completion processes. The study focuses on the crucial role of digital tools in analyzing offset well data and employing ILT analysis to optimize present and future drilling operations within an unconventional development drilling campaign. It explores the significance of these tools in analyzing historical well data to benchmark operations, identify ILT areas, and ultimately drive operational excellence and consistency. Case studies and empirical analyses underscore the significance of utilizing digital solutions for offset well data analysis and ILT identification, refining drilling strategies, and reducing non-productive time (NPT), thereby enhancing overall drilling efficiency. Utilizing the digital solution, this study analyzed two distinct drilling campaigns conducted by different teams, examining seven offset wells drilled by various rigs. The analysis includes investigating operational events through detailed key performance indicators (KPI) categories, ILT drivers, drilling practices, NPTs, and unplanned events. After identifying contributors to ILT and NPT, the study provided composite well time estimations for a UC gas HTHP well, enabling the establishment of detailed rig operations benchmarks allowing more accurate well delivery time estimations for future development drilling projects. Moreover, this paper emphasizes the importance of rig operation benchmarking, utilizing digital solutions to track, compare and enhance drilling performance against unconventional drilling wells record. The findings highlight that integrating digital solutions with ILT analysis and benchmarking enables more informed decision-making, leading to streamlined and optimized well operations within unconventional oil and gas drilling projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".