Optimizing Shale Oil Enhanced Recovery with Tuned Hydrocarbon Solvents
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Bibliographic record
Abstract
Abstract Shale oil development began around 2006, and since then more than 100,000 horizontal shale oil wells have been drilled in several basins across the U.S. All of these wells are characterizes by high initial flowrates and steep declines, and oil recovery of only 2-8%. as a percentage of OOIP in the well drainage or spacing area. Shale Enhanced oil recovery (EOR) has been conducted in more than 50 separate projects since around 2013, mostly via cyclic, or "Huff and Puff" operation using natural gas as the injection fluid. Recently, a few shale oil EOR projects have been successfully conducted using natural gas liquids, and these projects have demonstrated the ability to recover much more oil from these shale formations, and much faster than natural gas. Field tests to date show the key to optimizing the oil recovery from shale formations via Huff and Puff EOR is largely a function of the injectant fluid. Our objective is to illustrate via compositional reservoir simulation and core testing conducted in a representative Utica shale oil well how injected solvent composition may be adjusted, or "tuned" to optimize oil and gas recovery, at rates and volumes well in excess of that which may be recovered via dry or wet natural gas or carbon dioxide. Compositional reservoir simulation modeling was conducted in two Utica shale production history matched wells to evaluate enhanced oil recovery performance with varying hydrocarbon solvent injectant composition. Core testing of solvent huff and puff oil recovery was conducted in a representative Utica shale core with varying solvent composition to confirm the simulation model software results. Compositional simulation modeling of two Utica shale horizontal wells was conducted to obtain a history match on oil, gas, and water production. The matched models were then utilized to evaluate various liquid hydrocarbon solvent solutions for shale oil EOR. The modeling indicates that for these particular wells, incremental oil production of 300% over primary production may be achieved in the first five years of EOR operation. Using hydrocarbon solvent solutions optimized for each well and the in situ reservoir properties and hydrocarbon composition has a significant effect on oil recovery. The core tests, conducted with a more limited number of solvent compositions, confirmed the EOR performance as predicted via compositional reservoir simulation and the need for tuning of solvent composition for optimum oil recovery Tuned liquid hydrocarbon solvent huff and puff enhanced oil recovery in shale oil formations can significantly improve production and reserves. Tuning the liquid hydrocarbon solvent composition is required to achieve optimum oil recovery, as every shale oil play, and well location within the play, has specific rock and fluid characteristics. Tuned liquid hydrocarbon solvent huff and puff EOR has numerous advantages over cyclic gas injection, such as much greater oil recovery, much better economics/lower cost per barrel, better injection fluid containment, precludes the need for artificial lift, ability to optimize each cycle by integration with compositional reservoir simulation modeling, and lower emissions. Compositional reservoir simulation and core testing of a Utica shale core as described herein confirm the need for liquid hydrocarbon solvent composition tuning, which should enable recovery of far more oil, earlier and at lower cost, greatly improving profitability and extending the life of shale oil wells by several years, while precluding the need for artificial lift.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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