Determination of Thermal Maturity and Maturation Trajectories in Shale Petroleum Reservoirs with the Use of Modified Pickett Plots
Bibliographic record
Abstract
Abstract This paper develops innovative methods for analysis of some important exploration and production problems in shale petroleum reservoirs such as the determination of burial maturity and maturation trajectories, and determination of sweet spots with the use of Modified Pickett plots. The methods are explained with data from 226 Niobrara wells. Pickett plots have been used historically as a powerful tool for petrophysical analysis of well logs. The plots represent a snapshot on time that corresponds to the time when the well logs are run. Pickett plots rely on pattern recognition observable on log-log crossplots of porosity vs. true resistivity. The analysis has been used in the past primarily for determination of water saturation. However, the plot has been extended throughout the years for evaluation of other parameters of practical importance including, for example, permeability, process or delivery speed (permeability over porosity, k/ϕ), bulk volume of water (BVW) and pore throat apertures. In this paper, Pickett plots are extended from representing a snapshot on time to representing millions of years of burial and maturation trajectories. The proposed method is explained with data from 226 Niobrara wells. The modified Pickett plots leads to curved lines of water saturation (Sw) and BVW. The maturation trajectories on the plot help to explain compaction and why as maturation increases to generate oil and gas condensate, resistivity goes up. However, as maturation increases to generate dry gas in the Niobrara, resistivity decreases. The Lopatin time-temperature index (TTI) is also included in the modified Pickett plot. The proposed methodology also allows estimating changes in pore throat sizes updip and downdip of a structure, as well as in a basin flank. The ability to combine maturity, pore throat sizes, as well as porosity and process speed in a single graph makes the modified picket plot a valuable tool with potential to locate sweet spots in shale petroleum reservoirs to locate areas for possible improved oil recovery (IOR) and enhanced oil recovery (EOR). The key contributions of this paper are generating an original method for determining burial maturity and maturation trajectories of shale petroleum reservoirs with the use of modified Pickett plots, as well as determining changes in pore throat sizes in different places of a structure, which lead to the location of sweet spots. Although the methodology is explained with data of the Niobrara shales, it should have application in other shale petroleum reservoirs of the world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".