Quantifying Fluid Prediction Using Angle-Dependent Inversion Measured Against Log Fluid Substitutions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A Bright spot prospect was identified using 2D and 3D seismic. Due to the isolation of this prospect from existing infrastructure it is critical to be able to predict the type of hydrocarbon likely to be present. The approach taken to quantify hydrocarbon type included the following steps:reprocess a 2D seismic line which connects the well control to the prospect,use the well to model the response of different pore fluids in the reservoir quality sands,perform incident angle dependant inversions of the 2D seismic,statistically quantify and compare the results from the prospect with the model results,use the fluid probability at the prospect in the project risk assessment. We know that normal incidence seismic data is the response to the acoustic impedance, AI, of the geologic layers. However, at non-zero incident angles, the seismic data is the response to the elastic impedance1, EI, of the geologic layers. Because of the inherent pitfalls of using amplitude variation with offset2, AVO, a new approach was taken to quantify the fluid at the prospect. First, a pre-stack time migration of a 2D seismic line was created followed by near and far angle stacks. AI and EI volumes are then generated for the respective angle stacks. This process is called angle dependent inversion, ADI. AI and EI values are extracted for each CDP within the prospect. Log-based fluid substitution3,4,5 models are created to establish a range of AI and EI values for reservoir quality sands. Each model is displayed as a probability distribution function, PDF and compared to the extracted ADI values. The results indicate that it is unlikely that the prospect sand is brine filled. The most probable hydrocarbon in the prospect reservoir is gas. Introduction A deepwater Gulf of Mexico prospect was generated in which high amplitude seismic events were used to define the areal extent of a potential reservoir. The location of the prospect is over 30 miles from the nearest host platform. It was assumed that a single subsea completion would be used to develop a discovery. However, it was quickly realized that given the estimated volume of the reservoir and the tie back distance a gas discovery would be very commercial while an oil discovery would be equivalent to a dry hole. Trend analysis of the surrounding fields was not conclusive in predicting the most likely hydrocarbon type at the prospect. Therefore, a study was conducted in which modeled acoustic and elastic impedance for varying reservoir fluids were compared to extracted values from near and far angle seismic inversions in an attempt to predict the fluid type in the prospect. Although the prospect was mapped using a 3D data set it was concluded that a single 2D seismic line through the prospect would be sufficient to make the fluid prediction. A recent-vintage 2D line was selected that went through the center of the prospect and within 500 feet of our primary well control (Well 1).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it