Capital Project Advisors
Completed Projects

Modeling Facility Expansion Options under Uncertainty

The purpose of this project was to investigate the value of pre-investment in facility expansion options for a deepwater oil and gas facility. A discipline-integrated optimization model was specified to assist the project team during the concept comparison and selection phase. The intent was to assess how uncertainty in reservoir properties—such as recoverable reserves and reservoir continuity—impacts (i) optimal initial capacity, (ii) the likelihood of expansion, and (iii) the value of flexibility. The fast-solving optimization model generated solutions for initial production capacity, expected expansion size, and willingness to pay for expansion options. For facility engineers, subsurface engineers, and project managers, the workflow and model enabled a comprehensive exploration of the tradeoffs between initial investment and future flexibility for various subsurface scenarios.

Procedures for Assessing the Value of Oilfield Sensors

This project developed a framework for evaluating the value of real-time oilfield sensors using a value of information (VOI) framework, drawing on decision trees, probability information, and generic production response models to compare outcomes with and without sensors. This effort incorporated input from a panel of industry experts during a dedicated workshop to ensure realistic assumptions, and it was applied to four representative reservoir scenarios: CO2 injection in mature onshore oil fields, waterflooding in deepwater oil reservoirs, hydraulic fracturing in tight gas formations, and steam-assisted gravity drainage in heavy oil reservoirs. The work delivered a transparent methodology that project teams can readily implement, along with detailed demonstrations yielding expected economic and production benefits for each scenario.

The results provide a practical framework for quantifying the potential returns from sensor investments, helping to justify costs and optimize recovery strategies amid uncertainty. The VOI framework enables better decision-making among reservoir engineers, production technologists, and asset managers on technology adoption, and enriches discussions around project risks and opportunities.

Gas Storage Facility Design under Uncertainty

This investigation developed a streamlined workflow and discipline-integrated optimization model to evaluate the impacts of uncertainty on the optimal design of gas storage facilities. The goal of this effort was to improve decision-making during the screening and concept selection stages of project development by systematically evaluating uncertain variables such as reservoir properties, well performance, and market demand, using a fast-solving model that incorporated reservoir response, facility options and constraints, and economic criteria. The project delivered a practical workflow that project teams can use to assess, rank, and optimize competing facility design configurations under various specifications of the uncertainty. 

Development of Industry-Aligned Cost and Schedule Estimating Guidelines and Tools

This multi-year project involved the development of new cost and schedule estimating guidelines for oil and gas well construction projects, including P&A activities. All aspects of estimating were addressed, including accuracy ranges, contingency planning, estimate classifications, and technical assurance processes. The guidelines also defined roles and responsibilities for stakeholders such as engineers, technical authorities, and estimating SMEs. The goal was to achieve internal alignment across a global portfolio of projects and with external best practices (e.g. AACE). The project delivered standardized frameworks, detailed guidance, and numerous tools to support development of unbiased and credible cost and schedule estimates.

​​

Engineers, technical assurance teams, and project managers have successfully implemented the guidelines, resulting in industry-leading estimating accuracy for the client (as measured by IPA, Inc.). Accurate cost and schedule estimates support better decision analysis regarding investment decisions, budgets, risk management, and resource planning for all phases of asset development.

Models for Screening Pre-Investment Alternatives for In-Well Treatment Functionality

The first part of this two-part project developed a stochastic mixed-integer program to analyze the risk of production impairment from halite deposition and the impact of this risk on initial capital investment decisions. This effort delivered a robust sensitivity analysis to explore how varying subsurface assumptions affects optimal initial investment decisions, providing decision-makers with a valuable tool to navigate uncertainty.

​

The second part of this project added a complementary perspective by estimating the potential value of information (VOI) that would derive from new data on production fluids and associated estimates of the likelihood of halite deposition and production impairment. The VOI analysis leveraged the stochastic program such that decision-maker behavior was modeled systematically. The analysis delivered a quantitative framework to assess benefit-cost tradeoffs, and provided decision-makers with practical guidance on information collection strategies.

​

The approach developed and deployed in this project strengthens capital investment decision-making when there are material uncertainties in inputs. The results provide a quantitative basis for balancing initial investments with recourse options, and also an understanding of how new information can play a role. Owners and decision-makers can benefit from this kind of analysis because it enables more informed and economically sound capital allocation strategies within and between projects.

Field Development Optimization under Uncertainty: Screening Models for Decision Making

In this project, a discipline-integrated asset model was specified for a greenfield offshore oil development. The purpose was to support early decision-making given significant uncertainties in reservoir properties because of limited appraisal information. The project focused on creating a physics-based optimization framework that coupled reservoir dynamics, facility constraints, and economic criteria, allowing for endogenous determination of variables such as production rates, drilling schedules, and future facility expansion. The model served as the core of an uncertainty analysis workflow that evaluated critical investment decisions, including optimal initial facility capacity, the optimal number of pre-drilled wells, and the deployment of drilling rigs, while addressing uncertainties such as reservoir thickness and inter-compartmental transmissibility.

​​

The results provided decision-makers with insights into managing uncertainties in early-stage field development, leading to more informed and efficient planning. The model enabled faster evaluations and better alignment of development strategies with subsurface uncertainty, ultimately aiding in risk mitigation and the maximization of project value.

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

The objective of this investigation was to compare competing methods of optimization under uncertainty for oil and gas exploration and production projects. The primary goal was to compare and contrast Monte Carlo simulation and stochastic programming. The work developed practical workflows that integrate fast-solving reservoir models, facility design variables, and economic information, enabling a robust assessment of the effect of uncertainty on design and operational decisions. The analysis provided detailed examples, including specification of a multi-compartment and multilayer reservoir model, demonstrating how these methods can be applied to real-world problems involving multiphase flow and dynamic reservoir behavior.

The results offer valuable guidance for handling complex uncertainties in reservoir and project development, and for selecting appropriate optimization techniques, helping to balance computational efficiency with the need for detailed insights into project risks and outcomes. The workflows support better-informed strategies for capital investment and risk management, enhancing overall project efficiency.

The Economic Viability of Offshore Wind Projects

This project developed a comprehensive workflow to assess the economic viability of offshore wind projects. The study developed new models to evaluate numerous hypothetical projects, focusing on net present value (NPV) as the primary economic metric. The analysis specified a comprehensive economic model and included a complementary uncertainty analysis workflow designed to deliver probability distributions of NPV. The workflow enables rapid analysis of the impacts of uncertainty in electricity prices, wind speeds, and tax incentives, along with options to solve for various thresholds that could encourage investment in offshore wind initiatives in the United States. The results provide a valuable framework for project developers to evaluate the economic potential of offshore wind projects, offering insights that guide more informed project- and portfolio-level investment decisions. 

A Guide for the Estimation and Application of Learning Curves for Unconventional Oil and Gas Projects

This project developed a comprehensive framework and guidelines to standardize well cost and schedule estimation for high-density well construction campaigns in unconventional assets, with a primary focus on integrating learning curve modeling into life-cycle development cost analysis. The guidelines provide: a theoretical foundation for learning curves, original research to estimate learning curve coefficients based on historical performance data, specification of mathematical models and user-friendly tools to project performance improvements over cumulative activity, and factors to consider when deviating from default assumptions. Both drilling and completion phases were evaluated. Well engineers, asset teams, and financial planners in the client organization benefitted from these standards, as the framework improves estimate accuracy and facilitates consistent inter-asset economic comparisons.

Arctic Operations: Probabilistic Modeling of Weather, Vessel Response, and Drilling Operations to Support Decision Analysis 

This investigation specified a probabilistic simulation workflow to evaluate metocean conditions and their operational impacts on offshore drilling in the Arctic region, specifically to aid decision-making in oil and gas mega-projects. The analysis integrates historical data on weather patterns (such as wave direction and height), ice conditions, vessel responses, and decision-maker interventions to model the complexities of drilling operations. By integrating Monte Carlo simulations with decision-making, the workflow produces estimates of the expected duration and variability of the operating season, along with probabilities for (i) the completion of drilling activities of varying durations within a single season, and (ii) the expected number of seasons to complete a sequence of activities. 

The results provide practical guidance for navigating uncertainties in Arctic environments, improving cost estimating and schedule accuracy—and credibility with all stakeholders. Oil and gas companies, particularly drilling engineers and project managers involved in Arctic operations can benefit from this approach, as the insights support strategic planning, economic evaluations, and day-to-day operational decisions to optimize resource allocation and mitigate risks. This analysis served as a material input into a Board-level decision analysis and subsequent exit decision at the client.