Integrated reservoir characterization and simulation studies in stripper oil and gas fields
Bibliographic record
Abstract
The demand for oil and gas is increasing yearly, whereas proven oil and gas\nreserves are being depleted. The potential of stripper oil and gas fields to supplement the\nnational energy supply is large. In 2006, stripper wells accounted for 15% and 8% of US\noil and gas production, respectively. With increasing energy demand and current high oil\nand gas prices, integrated reservoir studies, secondary and tertiary recovery methods,\nand infill drilling are becoming more common as operators strive to increase recovery\nfrom stripper oil and gas fields. The primary objective of this research was to support\noptimized production of oil and gas from stripper well fields by evaluating one stripper\ngas field and one stripper oil field.\nFor the stripper gas field, I integrated geologic and engineering data to build a\ndetailed reservoir characterization model of the Second White Specks (SSPK) reservoir\nin Garden Plains field, Alberta, Canada. The objectives of this model were to provide\ninsights to controls on gas production and to validate a simulation-based method of infill\ndrilling assessment. SSPK was subdivided into Units A ? D using well-log facies. Units A and B are the main producing units. Unit A has better reservoir quality and\nlateral continuity than Unit B. Gas production is related primarily to porosity-netthickness\nproduct and permeability and secondarily to structural position, minor\nstructural features, and initial reservoir pressure.\nFor the stripper oil field, I evaluated the Green River formation in the Wells\nDraw area of Monument Butte field, Utah, to determine interwell connectivity and to\nassess optimal recovery strategies. A 3D geostatistical model was built, and geological\nrealizations were ranked using production history matching with streamline simulation.\nInterwell connectivity was demonstrated for only major sands and it increases as well\nspacing decreases. Overall connectivity is low for the 22 reservoir zones in the study\narea. A water-flood-only strategy provides more oil recovery than a primary-then-waterflood\nstrategy over the life of the field. For new development areas, water flooding or\nconverting producers to injectors should start within 6 months of initial production. Infill\ndrilling may effectively produce unswept oil and double oil recovery. CO2 injection is\nmuch more efficient than N2 and CH4 injection. Water-alternating-CO2 injection is\nsuperior to continuous CO2 injection in oil recovery.\nThe results of this study can be used to optimize production from Garden Plains\nand Monument Butte fields. Moreover, these results should be applicable to similar\nstripper gas and oil field fields. Together, the two studies demonstrate the utility of\nintegrated reservoir studies (from geology to engineering) for improving oil and gas\nrecovery.
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 | 0.001 |
| 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".