MétaCan
Menu
Back to cohort
Record W4390116612 · doi:10.2118/212745-pa

Evaluation of Flow Units and Capillary Pressures of the Giant Chicontepec Tight Oil Paleochannel in Mexico and a Fresh Look at Drilling and Completions

2023· article· en· W4390116612 on OpenAlexaffabout
Alejandra Gutierrez Oseguera, Roberto Aguilera

Bibliographic record

VenueSPE Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPalaeochannelGeologyPetroleum engineeringDrillingCapillary pressureOil in placeGeochemistryStructural basinGeotechnical engineeringPetroleumPaleontologyPorous mediumPorosityFluvialEngineering

Abstract

fetched live from OpenAlex

Summary The Chicontepec Paleochannel in Mexico is a giant shaly sandstone reservoir with volumes of original oil in place (OOIP) ranging between 137 and 59 billion STB (Guzmán 2022). However, the oil recoveries are very small, ranging between 0.32% and 0.75% of the OOIP. Under these conditions, consistent interpretation of flow units and mercury injection capillary pressures up to 55,000 psi provide useful information that helps in deciphering the rock quality and pore sizes at levels that might not be reached by thin-section petrography. This is important because the Chicontepec Paleochannel (Misantla-Tampico Basin) has been recently equated to the Permian Basin in the United States and has been termed by Guzman (2022) “a premier super basin in waiting.” The current cumulative oil production of Chicontepec is 440 million STB. Although it is a significant volume, it represents a very small percentage of recovery from the reservoir (0.32–0.75% of the OOIP). To help improve recovery, a method is developed for characterizing the tight Chicontepec Paleochannel using flow units and capillary pressures. Like in the case of many tight unconventional reservoirs, the capillary pressures can go to very high values, reaching 55,000 psi in the Chicontepec case. Therefore, a special procedure is developed to generate a consistent interpretation of all the available capillary pressure curves for the entire range of pressures. The results highlight the important oil recovery potential. The assessment is supported by quantitative formation evaluation work performed by Gutierrez Oseguera and Aguilera (2023). Although natural fractures are present, most wells must be hydraulically fractured to achieve commercial success. Process or delivery speed (the ratio of permeability and porosity) for the Chicontepec samples used in the capillary pressure experimental work range between 159.1 md and 0.17 md (porosity in the denominator is a fraction). Flow units show pore throat radii (rp35) range from less than 0.1 µm to about 4.5 µm. These values and flow units compare well with data available for prolific unconventional reservoirs such as the Cardium sandstone in Canada and the giant Permian Basin in the United States. The radius rp35 refers to the pore throat radius at 35% cumulative pore volume (PV) of injected mercury. This is different from rp also discussed in this paper, which is the pore throat radius at any water saturation (for example, at 40% water saturation). Thus, in the case where water saturation is 65%, rp is equal to rp35. The novelty of this study is the development of a consistent procedure for interpreting the entire range of pressures measured during mercury injection capillary pressures. Such pressures go up to 55,000 psi for the core samples considered in this study. The integration with flow units and formation evaluation suggests that the potential of the Chicontepec unconventional reservoirs can rival successful results obtained in the Cardium sandstone and the Permian Basin. The key ideas discussed in this paper for improving Chicontepec oil recovery include specialized petrophysical evaluation, determination of flow units and capillary pressures, improved drilling and completion methods, and geological support.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.238
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueSPE JournalSame topicHydrocarbon exploration and reservoir analysisFrench-language works237,207