Real-Time Downhole Monitoring Of Hydraulic Fracturing Treatments Using Fibre Optic Distributed Temperature and Acoustic Sensing
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Bibliographic record
Abstract
Abstract In order to make commercial and development decisions effectively and more rapidly, new appraisal and testing technologies are needed to maximize early data collection and subsequent subsurface understanding as quickly as possible. For Unconventional Gas and Light Tight Oil (UGLTO) projects, some of this critical data can be derived from hydraulic fracture stimulation and inflow profiling activities. For UGLTO projects, achieving an optimum hydraulic fracture stimulation is a continuous endeavor beginning as early as possible; and balancing the cost of completion vs. production performance is critical as the completion/stimulation is a large cost component of the well and heavily influences production rate/ultimate recovery. The fast paced development and introduction of new completion technologies requires diagnostic technology that can help us understand stimulation effectiveness, assess new completion technologies, and evaluate which zones are the most productive. One emerging technology, fibre optic distributed sensing has the potential of providing key insights during both the hydraulic fracturing and initial flowback. The passive nature of fibre optic sensors allows intervention-free surveillance, which makes fibre-optic technology an effective platform for permanent sensing in producing wells. Until recently, the oil & gas industry fibre optic sensing technology has focused mainly on temperature (DTS) profiling along the wellbore. In 2009, it was first demonstrated how fibre optic distributed acoustic sensing (DAS) can also be used for downhole applications. Where hydraulic fracture diagnostics based on DTS alone in the past sometimes yielded ambiguous results, the combination of both acoustic and temperature sensing provides a step-change improvement in the ability to perform real-time and post-job diagnostics & analyses of the stimulation. The different horizontal well case studies presented in this paper will illustrate how the combination of DTS and DAS has the potential to enhance the monitoring, assessment, and optimization of openhole and limited entry designed hydraulic fracture stimulation treatments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it