Attempts to Understand Reservoir Communication Using Interwell Chemical Tracers and the Coherence Cube
Bibliographic record
Abstract
Abstract Tertiary waterfloods are an efficient and often economical method to increase reserves allocation and decrease Finding and Development costs. The use of Chemical Interwell Tracers (CIT) can be an excellent production enhancement tool for reservoir management. A CIT Program can be implemented at any time during the life of the flood to better understand fluid flow within a producing reservoir. Injecting chemical tracers into injection wells and catching water samples from producing wells offers an opportunity to determine, through direct fluid measurement, the differing fluid flow patterns in a reservoir. Tracers can determine what injected fluid is being produced at which producing well(s) in the field, but they do not allow for determining the actual flow path of the injected fluid. By integrating the chemical tracer results with the Coherence Cube ™, it is possible to determine the actual pathway of the fluid movement within the reservoir. This additional information can be extremely valuable in optimizing production and increasing the placement efficiency of injected water. This paper illustrates how combining the separate, proven technologies of Chemical Interwell Tracers and the Coherence Cube ™, can serve as an excellent reservoir management tool for well placement, production enhancement and cost reduction. Introduction This paper will endeavor to show, with mixed results, the potential value in combining two distinct and separate technologies to help in understanding preferential flow in reservoirs. CHEMICAL INTERWELL TRACING - GENERAL DESCRIPTION Interwell tracer programs have been used in water flood operations to confirm a field's directional heterogeneities and/or flow paths and barriers. This has been accomplished by combining practical field knowledge with reservoir simulation to develop the "Spectraflood" Design and Analysis System". The SpectraFlood program models fluid flow through porous media, potential heterogeneity sensitivity using the Dykstra-Parsons heterogeneity coefficient, and tracer inter-zonal mixing, using a stream tube model in this particular case. The SpectraFlood model allows for the determination of the chemical tracer concentration and quantity required, as well as the theoretical breakthrough times at each producing well. The breakthrough times are used to establish a sample collection schedule to make sure the "tracer wave(s)" is(are) not missed. This design process also takes into account project economics, environmental factors, and tracer detection limits. The tracer detection limits have a built-in safety factor in order to capture a wide range of potential variation in results. This is important in order to have faith in the data, because if chemical tracer(s) are not detected at some producing well(s), the question " Was there enough tracer added to be able to detect it?" could be asked. Therefore, with the built-in tracer volume injected, the answer is always, " if the tracer did not show up in the produced water samples at a producer, then fluid from that injector did not go there". The family of chemical tracers was selected because they exhibited the following characteristics: [1]Eighteen (18) potential tracers (one tracer per injection well) which can be detected with a single analysis;Detectability at ultra-low concentrations of fifty (50) parts per trillion (ppt) under field conditions;Nontoxic and environmentally safe;
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".