New Hybrid Workflow for Field Development Planning and Execution: Early Time Fracs vs Long Term Production
Bibliographic record
Abstract
Abstract Recently, a hybrid workflow for cost-effective and efficient field development planning and execution was introduced to accelerate learnings from post-frac data and to inform development decisions, without waiting for long-term production. This workflow combines typically gathered field data (i.e. geomechanics, flowback and short-term production) with existing integrated workflows to significantly reduce uncertainty and execution time associated with today’s asset development workflows. Although powerful, this workflow lacks ties with long-term production data and does not account for variability in fluid properties across the asset, which limits wide-spread applicability. In this paper, the hybrid workflow will be extended to incorporate long-term production data. The extended workflow incorporates a series of correlations and asset maps which can be used to benchmark and improve completion design selection and performance, with the ultimate goal of streamlining asset development. The workflow includes the development and application of the following: 1) asset map of effective stress; 2) asset map of permeability 3) fracture area correlation (effective stress vs. fracture area); 4) normalized rate correlation (90-day production rate vs. linear flow parameter, LFP); and 5) long-term production correlation (extended production vs LFP). Component 5 is the key new addition to the workflow, which adds significant value and improves the widespread applicability in complex assets. A mass-flow rate approach will also be utilized to enhance the ability to compare productivity from wells producing from varying fluid types. By integrating flowback analysis (FBA) with other multi-disciplinary analysis methods into this enhanced hybrid workflow, operators can effectively improve development efficiency and performance of their assets over both short and long-term production. The enhanced hybrid workflow will be demonstrated using a case study from a high-profile asset in a prolific North American unconventional reservoir. The studied asset shows significant variability in fluid properties, geomechanics and productivity. Using this workflow, operators can connect initial reservoir and fluid properties to long term asset performance and by integrating FBA, link geomechanical properties to stimulated area with a given design to achieve target frac and production performance. Incorporating this workflow also assists with minimizing both G&A and development capital costs. By incorporating long-term production into the hybrid workflow, the widespread applicability is significantly improved, while also providing a key link between early-time frac performance and long-term productivity. Using this enhanced hybrid workflow, operators can unlock the full potential of their assets, while dramatically improving the efficiency and cost-effectiveness of asset 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".