Developing The Business Case For Water Management Planning Integration
Bibliographic record
Abstract
Abstract It is well documented that the cost of managing produced water amounts to a significant portion of the operating cost of an unconventional well over its lifetime. The greatest factor attributing to this cost is transportation, which can be addressed through logistical planning. However, effective logistical planning begins at the early phases of a project during reservoir engineering and drilling and production scheduling. But making the case for early phase water management planning can be challenging when there is limited field data and engineering priorities have not yet been established. This paper will look at how dynamic simulation modeling can be used to cost effectively optimize facility engineering while taking into consideration water management planning objectives. Water management and facilities planning is an iterative process that is affected by many different inputs, ranging from environmental constraints to oil and gas production targets to power and energy demands. Putting these into a dynamic context that supports early phase scenario planning allows diverse members of the development team (reservoir, completions, facilities, water and environmental engineers) to understand the impacts of various constraints on the development schedule, well production, and capital and operating costs. Having this information in hand early in the project also allows the team to work together to quantify the impacts of various water management scenarios on exploration and production and conversely, the impact of the exploration and production decisions on future water sourcing and reuse opportunities. Using a dynamic simulation modeling process to evaluate these scenarios and provide meaningful output and documentation can help management to prioritize their cost savings goals, balance trade-offs between water management and facility engineering objectives and communicate their business decisions to the necessary stakeholders. In addition to evaluating the engineering viability and economic impacts, a dynamic simulation model can also take into consideration the area-wide impacts to water, air and the ecosystem under various planning scenarios, and can be used to support play-based water management planning and permitting and industry collaboration.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".