Characterization of the Giant Chicontepec Tight Oil Paleochannel in Mexico and Integration with Actual Cumulative Oil Production
Bibliographic record
Abstract
Summary The Chicontepec paleochannel contains unconventional tight oil shaly sandstone reservoirs also characterized by natural fractures of tectonic origin. Chicontepec ranks as a giant reservoir with volumes of original oil in place (OOIP) ranging between 137,300 and 59,000 million STB (Guzman 2019). Although the cumulative oil is significant (440.38 million STB), it only represents 0.32 to 0.75% of the OOIP. The objective of this study is to develop a new characterization methodology with a view to increase oil recovery from Chicontepec. OOIP in Chicontepec paleochannels was estimated originally at 137,300 million STB. Despite several studies using state-of-the-art methodologies and contracting major oilfield services companies to test new technologies and significant investments, the OOIP was decreased recently to 59,000 million STB due to lack of any significant success on the implemented projects. This study shows that the key to success is understanding the contribution of natural fractures. This is demonstrated for the case of Chicontepec with a new dual-porosity petrophysical model for naturally fractured laminar shaly sandstone reservoirs developed in this study. The model assumes that matrix and fractures are in parallel. Laminar shaliness is handled with a parameter (Alam) that is a function of true and shale resistivities, and fractional shale volume (Vsh). The methodology integrates data from observations in outcrops, quantitative evaluation of cores, well logs, and actual production data. Past Chicontepec studies have assumed that the porosity exponent (m) in Archie and shaly sandstone equations is constant. However, core studies indicate that Chicontepec m values become smaller as porosity decreases. The proposed dual-porosity petrophysical model, when applied to actual Chicontepec wells, matches properly the laboratory values of m and generates results that generally compare well with actual production data (e.g., the larger the value of fracture partitioning, the larger is the cumulative oil production). Pattern recognition allows estimating fracture intensity with a partitioning coefficient, which is calculated as the ratio of fracture porosity to total porosity. The new contribution of this manuscript is the development of a petrophysical dual-porosity model for naturally fractured shaly sandstone reservoirs that integrates variable values of m from cores, fracture intensity, and cumulative production of individual Chicontepec wells. Our detailed review of the literature indicates that this methodology has not been published previously.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".