Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2
Bibliographic record
Abstract
The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".