A Model for Optimizing Energy Investments and Policy Under Uncertainty with Application to Saudi Arabia

An energy producer must determine optimal energy investment strategies in order to maximize the value of its energy portfolio. Determining optimal investment strategies is challenging. One of the main challenges is the large uncertainty in many of the parameters involved in the optimization process. Existing large-scale energy models are mostly deterministic and thus have limited capability for assessing uncertainty. Modelers usually use scenario analysis to address model input uncertainty. In this research, I developed a probabilistic model for optimizing energy investments and policies from an energy producer’s perspective. The model uses a top-down approach to probabilistically forecast primary energy demand. Distributions rather than static values are used to model uncertainty in the input variables. The model can be applied to a country-level energy system. It maximizes the portfolio expected net present value (ENPV) while ensuring energy sustainability. The model was built in MSExcel® using the @RISK Palisade add-in, which is capable of modeling uncertain parameters and performing stochastic simulation optimization. The model was applied to Saudi Arabia to determine its optimum energy investment strategy, determine the value of investing in alternative energy sources, and compare deterministic and probabilistic modeling approaches. The model, given its assumptions and limitations, suggests that Saudi Arabia should keep its oil production capacity at 12.5 million barrels per day, especially in the short term. It also suggests that most of the future power-generation (electricity) demand in Saudi Arabia should be met using alternative-energy sources (nuclear, solar, and wind). Otherwise, large gas production is required to meet such demand. In addition, comparing probabilistic to deterministic model results shows that deterministic models may overestimate total portfolio ENPV and underestimate future investments needed to meet projected power demand. A primary contribution of this work is rigorously addressing uncertainty quantification in energy modeling. Building probabilistic energy models is one of the challenges facing the industry today. The model is also the first, to the best of my knowledge, that attempts to optimize Saudi Arabia’s energy portfolio using a probabilistic approach and addressing the value of investing in alternative energy sources

A Triple-Porosity Model for Fractured Horizontal Wells

Fractured reservoirs have been traditionally idealized using dual-porosity models. In these models, all matrix and fractures systems have identical properties. However, it is not uncommon for naturally fractured reservoirs to have orthogonal fractures with different properties. In addition, for hydraulically fractured reservoirs that have preexisting natural fractures such as shale gas reservoirs, it is almost certain that these types of fractures are present. Therefore, a triple-porosity (dual-fracture) model is developed in this work for characterizing fractured reservoirs with different fractures properties. The model consists of three contiguous porous media: the matrix, less permeable micro-fractures and more permeable macro-fractures. Only the macro-fractures produce to the well while they are fed by the micro-fractures only. Consequently, the matrix feeds the micro-fractures only. Therefore, the flow is sequential from one medium to the other. Four sub-models are derived based on the interporosity flow assumption between adjacent media, i.e., pseudosteady state or transient flow assumption. These are fully transient flow model (Model 1), fully pseudosteady state flow model (Model 4) and two mixed flow models (Model 2 and 3). The solutions were mainly derived for linear flow which makes this model the first triple-porosity model for linear reservoirs. In addition, the Laplace domain solutions are also new and have not been presented in the literature before in this form. Model 1 is used to analyze fractured shale gas horizontal wells. Non-linear regression using least absolute value method is used to match field data, mainly gas rate. Once a match is achieved, the well model is completely described. Consequently, original gas in place (OGIP) can be estimated and well future performance can be forecasted

The Importance of Preventive Medicine in Family Practice: A Review of Current Guidelines and Recommendations

Prevention is seen as a critical topic in family practice. Primordial prevention, primary prevention, secondary prevention, tertiary prevention, and quaternary prevention are all part of this strategy to disease prevention. To avoid the formation and development of risk factors, primary prevention focuses on addressing the fundamental causes and social determinants of disease. Primary prevention is the practice of preventing illnesses before they arise via the use of treatments such as immunizations and health education. Secondary prevention focuses on illness identification and intervention as early as possible to avoid disease development. Tertiary prevention addresses illness outcomes by restoring health and offering rehabilitation. Finally, quaternary prevention seeks to safeguard patients against needless medical treatments and the harm caused by over-medicating. Risks frequently rise in tandem with frailty and comorbidities. In contrast, advantages frequently drop as life expectancy increases. Preventive management strategies should consider the patient's viewpoint and be mutually agreed upon. Healthcare providers must prioritize the deployment of preventive care services, even when clinical treatments are required, in order to overcome preventive care hurdles. Healthcare practitioners may play a critical role in illness prevention and contribute to family well-being by investing in preventive care and executing these measures

A Model for Optimizing Energy Investments and Policy Under Uncertainty with Application to Saudi Arabia

An energy producer must determine optimal energy investment strategies in order to maximize the value of its energy portfolio. Determining optimal investment strategies is challenging. One of the main challenges is the large uncertainty in many of the parameters involved in the optimization process. Existing large-scale energy models are mostly deterministic and thus have limited capability for assessing uncertainty. Modelers usually use scenario analysis to address model input uncertainty. In this research, I developed a probabilistic model for optimizing energy investments and policies from an energy producer’s perspective. The model uses a top-down approach to probabilistically forecast primary energy demand. Distributions rather than static values are used to model uncertainty in the input variables. The model can be applied to a country-level energy system. It maximizes the portfolio expected net present value (ENPV) while ensuring energy sustainability. The model was built in MSExcel® using the @RISK Palisade add-in, which is capable of modeling uncertain parameters and performing stochastic simulation optimization. The model was applied to Saudi Arabia to determine its optimum energy investment strategy, determine the value of investing in alternative energy sources, and compare deterministic and probabilistic modeling approaches. The model, given its assumptions and limitations, suggests that Saudi Arabia should keep its oil production capacity at 12.5 million barrels per day, especially in the short term. It also suggests that most of the future power-generation (electricity) demand in Saudi Arabia should be met using alternative-energy sources (nuclear, solar, and wind). Otherwise, large gas production is required to meet such demand. In addition, comparing probabilistic to deterministic model results shows that deterministic models may overestimate total portfolio ENPV and underestimate future investments needed to meet projected power demand. A primary contribution of this work is rigorously addressing uncertainty quantification in energy modeling. Building probabilistic energy models is one of the challenges facing the industry today. The model is also the first, to the best of my knowledge, that attempts to optimize Saudi Arabia’s energy portfolio using a probabilistic approach and addressing the value of investing in alternative energy sources

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population