Far-Field Proppant Detection Using Electromagnetic Methods - Latest Field Results
Bibliographic record
Abstract
Abstract Over 100 billion lbm of proppant are placed annually in wells across the globe, with the majority in unconventional reservoirs. The location of the proppant in these horizontal wells and formations is critical to understanding reservoir drainage, well spacing and stage spacing. However, for many years proppant detection has primarily been limited to near-wellbore measurements. A novel method to detect proppant in the far-field has been developed and is the subject of this paper. The method to detect proppant that has been developed utilizes electro-magnetic methods. This technology entails using a transmitter source and an array of electric- and magnetic-field sensors located at the surface. A current signal with a unique wave form and frequency is transmitted to the bottom of the wellbore via a standard electric line unit. In addition, an electrically conductive proppant is pumped into the stage(s) of interest. The electric- and magnetic-fields are measured both before and after the detectable proppant stages, and a novel analysis method is then employed to process and invert this differenced data to create an image of the propped reservoir volume. This technology is the product of years of development of computer models capable of forward modeling this technique. Once this modeling was completed, an initial field test was performed in west Texas, with a preliminary analysis of this work presented in a previous paper. Since that paper, however, additional processing of the data has yielded a much more detailed image of the proppant location in this Bone Springs well. In addition, a subsequent field application has been performed in a major basin in the Northeast US. Multiple stages received detectable proppant of varying stage volumes and the analysis has shown a detailed image of the proppant location in that wellbore also. In addition, the initial west Texas field test employed only electric-field sensors, while this latest test employed both electric- and magnetic-field receivers. Numerical simulations and field results indicate the percentage difference between pre- and post-frac results are two times higher using magnetic versus electric field sensors. This paper will review the technology development and methods, it will present the latest imaging from the initial west Texas test, and it will describe the latest learnings from the most recent field test. This paper should be beneficial to all completions and development personnel who are interested in knowing where proppant is located in their fractures. This technology has the potential to assist in understanding well drainage and spacing, stage and perf cluster spacing, vertical fracture coverage as well as the impact of fracture design changes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".