Well Spacing Optimization for Tight Sandstone Gas Reservoir
Bibliographic record
Abstract
In the past five years, tight sandstone gas reserves amounted to almost 50% of the newly proved natural gas reserves of PetroChina Co., Ltd. and will be one of the main targets of natural gas production for a long term.Means to determine the reasonable well spacing are the key topics in the development of tight sandstone gas reservoir. Relationships among well spacing, gas recovery and ultimate accumulation production are involved. For tight sandstone reservoir, because of its lateral permeability heterogeneity, flowing barrier which decreases the effective drainage area for gas depletion is frequently detected. Wide well spacing would result in low efficiency for producing reserves and decrease the ultimate recovery factor. Small well spacing would probably cause pressure conductance between adjacent wells and decrease the ultimate cumulative recovery of individual gas well, which would decrease the economic returns seriously. Thus, ways to determine the optimized well spacing is a great challenge in the development of tight sandstone reservoir.Based on experiences in development of Sulige Gas field, reasonable well spacing can be properly determined from the following three aspects under precondition of maximum economic returns and ultimate recovery factor. The first one is that 3D geological model was utilized in the well pattern optimization process. Data of outcrops, cores and production of local infilling wells were used in the modeling. Micro-facies and reservoirs were accurately evaluated by determination of the scale of gas bearing sand bodies and its distribution. The second aspect is that through dynamic analysis of gas well production, three parameters including effective drainage area, recoverable reserves and influence area were calculated. The third one is that reasonable well spacing was determined on basis of the compromised researches of correlations between average investment, accumulative production rate, influence area and ultimate recovery factor.
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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".