Sustainable Management of Produced Water in Venezuela’s Major Hydrocarbon Basins (Eastern Venezuela and Maracaibo Basins): Exploring Opportunities for Hydrogen Generation, Geothermal Energy and Mineral Extraction
Bibliographic record
Abstract
Abstract Sustainable water management in hydrocarbon production poses a significant challenge for Venezuela’s oil and gas industry, particularly in the Eastern Venezuela and Maracaibo basins. Traditionally, the water lifecycle in the hydrocarbon industry has focused on phase separation (e.g., hydrocarbons and solids), treatment (e.g., emulsions), transport, disposal, storage, and utilization. However, water valorization techniques for renewable energy production have yet to be fully explored. This article introduces a workflow for hydrogen production, geothermal energy generation, and valuable mineral extraction from produced water in Venezuela, aiming to enhance water resource utilization and diversify energy sources with a low environmental impact. A comprehensive review is conducted on available information, including published technical reports and scientific publications, production profiles, and physicochemical water analyses. The conversion of existing wells in high-water zones and/or aquifers is emphasized, along with the techniques for hydrogen production through water electrolysis. The review also explores potential formations containing valuable metals and minerals, together with the techniques—both research-focused and industrial—used for the extraction and production of electrolytic hydrogen and minerals like lithium. Key factors influencing these processes are also identified. The results of this study suggest that several potential scenarios for managing produced water in Venezuela include hydrogen production, the use of geothermal energy through existing wells (with possible reconditioning), mineral extraction from produced brines, the application of alternative enhanced/improved oil recovery (EOR/IOR) processes, water use for agricultural purposes, among others. Current findings highlight the importance of considering physicochemical and geochemical reactions—such as precipitation of solids, scaling, mineralization, acid gas generation, and corrosion—along with the selection of suitable materials, management and storage of acidic gases, electrolyzer efficiency, and an effective underground-surface heat transfer system. These factors are crucial as they directly affect the efficiency, safety, and cost-effectiveness of the processes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".