Jonás Velasco

Natural gas production from mature fields is progressively shifting toward low-rate wells operating near their economic limit, creating challenges for long-term asset management. This study proposes an integrated stochastic decision-support framework combining Arps decline curve analysis, a calibrated Schwartz Type-I mean-reverting jump-diffusion model, Monte Carlo simulation, and Bellman dynamic programming to optimize marginal dry gas well management. The framework evaluates pipeline commercialization and a hybrid strategy integrating on-site electricity generation, while incorporating monetized environmental externalities associated with CO₂ emissions from gas combustion and potential post-abandonment CH₄ emissions. Application to the Mareógrafo 100 well in Mexico shows that the environmentally adjusted Bellman policy yields a mean NPV of USD 34.84 thousand, exceeding the comparable pipeline-only and hybrid strategies. Internalizing environmental costs reduces the mean optimal NPV by 46.6% relative to the economic-only formulation, while the mean abandonment time is approximately 200 days. Sensitivity analysis identifies electricity price, natural gas price, and pipeline distance as the dominant profitability drivers. The proposed framework provides a transferable methodology for jointly evaluating commercialization, environmental externalities, and abandonment decisions in mature dry gas fields under uncertainty.

In the capacitated arc routing problem (CARP), a fleet of capacitated vehicles based at a depot must cover the streets of a network where the demand is located at the lowest possible total cost. Waste collection, street sweeping, winter gritting, and mail delivery are among its best-known applications. This work introduces the CARP with vehicle dependence (CARP-VD), an extension in which the cost of servicing an edge, and that of traversing it without service, are specific to each vehicle type and formulates it as a mixed-integer linear program. A two-stage matheuristic is proposed: the first stage distributes the required edges among the vehicles without exceeding their capacities, and the second builds the route of each vehicle. A bound is derived that limits the optimality loss of this decomposition by its own deadheading cost. Both approaches are evaluated on 47 benchmark instances adapted from the literature under a common one-hour budget, and their robustness is assessed over six scenarios that vary the parameters of the adaptation. The matheuristic returns good-quality solutions in a fraction of the time on the smaller instances, and on those in which almost every edge requires service it improves the best solutions found by a commercial solver applied to the complete model by up to 44%.

A thermo-economic valuation framework is proposed to assign an economic value to industrial waste heat streams. The methodology integrates thermodynamic heat-quality degradation, represented through logarithmic mean temperature difference (LMTD)-based thermal decay, with market-indexed valuation using natural gas substitution benchmarks. To account for operational uncertainty, a Monte Carlo simulation and a two-state Markov chain were implemented to represent stochastic dispatch conditions of natural gas engines within an industrial park configuration. The results indicate that operational variability has a limited influence on waste heat valuation compared with the dominant effect of natural gas price indexation and transport-distance-related thermal degradation. Under the representative case study conditions, supplier revenues ranged from approximately USD 60,000 to USD 320,000 per year depending on prevailing gas prices, while consumers achieved estimated energy cost reductions between 22% and 62%. An exergy-based comparison was additionally performed to contextualize the thermodynamic quality of the recovered heat stream. The proposed framework provides a practical methodology for evaluating industrial waste heat under localized energy exchange and fuel substitution applications.

This study evaluates the technical and economic feasibility of using waste heat from combustion gases as an energy resource for cogeneration systems in Mexican industrial parks. A pricing model based on thermodynamic principles was developed to assign an economic value to waste heat. The model incorporates key parameters, including nitrogen oxide (NOx) concentration, thermal quality, delivery distance, and indexation to the price of natural gas. To account for operational and market uncertainty, a Monte Carlo simulation and a two-state Markov chain were implemented to represent the stochastic dispatch regimes of combustion engines. The results show that, compared to the dominant effect of natural gas price indexation, operational variability has a negligible impact on waste heat prices and annual revenues. When applying the model to a representative case study, two main economic benefits were estimated. Suppliers' annual revenues can range from US$60,000 to US$320,000 depending on the prevailing gas price, and consumers' potential energy cost reductions range from 22% to 62%. From an environmental perspective, the model estimates a reduction of up to 43.4 short tons (39.4 metric tons) of CO2 emissions per year. Additionally, the study establishes a structured valuation framework that could serve as a basis for integrating waste heat into formal energy markets. This framework could enable the development of pricing mechanisms and transform waste heat into a quantifiable, potentially tradable energy asset.

Precision agriculture employs sophisticated tools to optimize decision-making in farming, aiming to simultaneously improve crop yields and manage resources more effectively in a context of increasing scarcity and rising costs. A key aspect of precision agriculture is the delineation of site-specific management zones (SSMZs), which involves segmenting a field into areas that are homogeneous in terms of soil physicochemical properties. The problem of delineating SSMZ have been approached using a wide variety of methodologies, all of which, heuristic, focus on finding feasible solutions. Until this work, there was no exact algorithm or mathematical model that would allow for a point of comparison. This paper introduces a novel approach to tackle the delineation of SSMZ with orthogonal shapes through the development of a mixed integer nonlinear programming (MINLP) model. Small instances with different scenarios show the scope of the proposed approach and the significance of the results. It provides a structure for the SSMZ problem with orthogonal shapes and establishes a benchmark for evaluating the performance of heuristic solutions, metaheuristics, or hybrid approaches.