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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2605.26493 |
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| _version_ | 1866911718099124224 |
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| author | Cui, Junyi |
| author_facet | Cui, Junyi |
| contents | Annual oil and gas exploration planning involves selecting a limited portfolio of drilling and appraisal-related projects before geological outcomes are known. This decision is affected by uncertainties in geological success, reserve size, and economic value, while also subject to budget, well-count, success-rate, and reserve-reliability requirements. A strategy based only on expected value is therefore insufficient, as early drilling results may change the value of subsequent follow-up opportunities. This study develops a posterior-informed two-stage stochastic multi-objective optimization framework for exploration well selection under uncertainty. The first stage selects a here-and-now portfolio of frontier traps, appraisal projects, and mature appraisal units. After first-stage outcomes are observed, the second stage determines scenario-dependent recourse projects, including follow-up appraisal, reserve upgrading, conversion-to-proved reserves, rolling extension, and data re-evaluation projects. Geological learning is modeled using a logit-scale posterior updating mechanism that links first-stage success or failure to the success probabilities of related recourse projects. The model maximizes expected net present value and minimizes conditional value-at-risk, while imposing chance constraints on drilling success rate and individual and joint reserve targets. To solve the model, sample average approximation is combined with NSGA-II for first-stage portfolio search and a scenario-wise constrained 0-1 optimization procedure for second-stage evaluation. A numerical case study shows that the proposed framework provides an interpretable risk-return frontier and supports adaptive exploration planning under geological learning, downside-risk control, and reserve-reliability requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26493 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A two-stage stochastic programming framework for oil and gas exploration well portfolio optimization under geological and economic uncertainty Cui, Junyi Optimization and Control Annual oil and gas exploration planning involves selecting a limited portfolio of drilling and appraisal-related projects before geological outcomes are known. This decision is affected by uncertainties in geological success, reserve size, and economic value, while also subject to budget, well-count, success-rate, and reserve-reliability requirements. A strategy based only on expected value is therefore insufficient, as early drilling results may change the value of subsequent follow-up opportunities. This study develops a posterior-informed two-stage stochastic multi-objective optimization framework for exploration well selection under uncertainty. The first stage selects a here-and-now portfolio of frontier traps, appraisal projects, and mature appraisal units. After first-stage outcomes are observed, the second stage determines scenario-dependent recourse projects, including follow-up appraisal, reserve upgrading, conversion-to-proved reserves, rolling extension, and data re-evaluation projects. Geological learning is modeled using a logit-scale posterior updating mechanism that links first-stage success or failure to the success probabilities of related recourse projects. The model maximizes expected net present value and minimizes conditional value-at-risk, while imposing chance constraints on drilling success rate and individual and joint reserve targets. To solve the model, sample average approximation is combined with NSGA-II for first-stage portfolio search and a scenario-wise constrained 0-1 optimization procedure for second-stage evaluation. A numerical case study shows that the proposed framework provides an interpretable risk-return frontier and supports adaptive exploration planning under geological learning, downside-risk control, and reserve-reliability requirements. |
| title | A two-stage stochastic programming framework for oil and gas exploration well portfolio optimization under geological and economic uncertainty |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2605.26493 |