Recent Advancements in Far-Field Proppant Detection
Bibliographic record
Abstract
Abstract The combination of multistage hydraulic fracture treatments with horizontal drilling technology has been the primary driver to the successful development of resource plays. More than 85% of wells drilled in North America today employ these methods. However, while these technologies have been wildly successful, only recently has the industry begun to address in earnest, the efficiency of current practices. These completion and development optimization efforts require an understanding of which portions of the reservoir have not been adequately contacted/stimulated and are thereby failing to contribute to production, and ultimate hydrocarbon recovery. Understanding where the proppant is located, both near- and far-field, is the starting point for these evaluations, and is the basis for this paper. Traditional fracture mapping technologies provide indirect estimates of fluid distribution within the fracture network. However, there is little direct correlation between fluid distribution and proppant location, and since most unpropped portions of fractures rapidly collapse, identification of the proppant location better represents the region which contributes to ultimate recovery. Near-wellbore detection of proppant can provide insight into whether all perf clusters (in the case of plug and perf) have received proppant as well as the impacts of proppant overflush. Conversely, accurate determination of far-field proppant placement will affect everything from well and stage spacing, to stage design and refrac candidate selection, and allow significant optimization of diversion techniques. While knowledge of both near- and far-field proppant location is necessary for the industry to overcome the single-digit recovery factors that are now projected in many unconventional plays, far-field proppant detection techniques have been largely absent to date. This paper briefly reviews the current "state of the industry" regarding near-wellbore proppant detection technology. It then presents a novel far-field proppant detection technique which utilizes electro-magnetic differencing and a specialty detectable proppant. This includes a description of the technology as well as the methodology of the technique. In addition, the paper reviews the design and results from a recent (first-ever) field deployment of this technology in a horizontal Permian Basin well. Visualization of the proppant in the far-field is also shown. This paper should be beneficial to all engineers and technologists currently interested in evaluating completion efficiencies as well as fracture stimulation effectiveness. Understanding proppant location in both the near- and far-field regions has significant impact on well spacing, stage and perf cluster spacing, and ultimate recovery from stimulated horizontal wells.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".