Far-Field Proppant Detection Using Electromagnetic Methods: Latest Field Results
Bibliographic record
Abstract
Summary More than 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 proppant-detection method developed uses electromagnetic (EM) methods. This technology entails using a transmitter source and an array of electric- and magnetic-field sensors 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 (E-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 used to process and invert these differenced data to create an image of the propped reservoir volume (PRV). 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 (WTX), with a preliminary analysis of this work presented in a previous paper (Palisch et al. 2016). 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 northeastern US. Multiple stages received detectable proppant of varying stage volumes, and the analysis has also shown a detailed image of the proppant location in that wellbore. Furthermore, the initial field test in WTX used only electric-field sensors, whereas this latest test used both electric- and magnetic-field receivers. The authors’ numerical simulations coupled with the field results indicate the percentage difference between prefracture and post-fracture results is two times higher using magnetic- vs. electric-field sensors. This paper will review the technology development and methods, will present the latest imaging from the initial WTX test, and 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 in their fractures. This technology has the potential to assist in understanding well drainage and spacing, stage and perforation-cluster spacing, vertical fracture coverage, and the effect 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".