Integrating Technology and Organizational Alignment for a Portfolio Implementation

Fig. 1—When capital is a limited resource, a bottoms-up approach to budget proposals does not provide a clear view to the optimal suite of projects to fund. CAPEX=capital expenditure.

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Choosing the best projects to fund is easy. The true challenge is weighing the complexities of the projects that are at the threshold for funding in a capital-constrained environment. One exploration-and-production (E&P) company has found that implementing and sustaining a portfolio process require technical solutions and application of best practices for three critical elements: production forecasting, project modeling and economic evaluation, and portfolio management and decision making.

Introduction

When capital is a limited resource, a ­bottoms-up approach to budget proposals does not provide a clear view to the optimal suite of projects to fund. Fig. 1 above illustrates this concept, in which each business unit (BU) submits an independent budget for funding consideration. While the sum of the requests is, for this example, $6 billion, the corporation only has $4 billion to provide in order to meet cash-flow expectations.

While each budget proposal may contain many projects that surpass internal economic hurdles, the total sum of requested capital among all submitted budgets cannot be provided. In contrast, the corporate portfolio has clear sight of which projects contribute to corporate values, goals, and targets, and can deselect marginal projects in order to meet the total capital constraint. This approach is particularly important and effective in exercising capital discipline. 

Optimization of the portfolio is an exercise in linear optimization to maximize total discounted net present value. Establishing a corporate portfolio is about more than merely the tools used for linear optimization of the opportunities. A portfolio must start with solutions for the complexities involved in the characterization of the projects themselves, evaluation of uncertainty and thresholds for decisions, and discretization of possible outcomes into cash flows that may be evaluated in the portfolio. 

Business challenges involved in successful implementation of a corporate-portfolio process include accurate production forecasting, project ­modeling and economic evaluation, portfolio management, and assessment of organizational alignment and resources (e.g., how decentralized BUs can coordinate effectively with others within a larger organization).

Solutions (Technology)

Production Forecasting. Monte Carlo simulation methods have been applied for both unconventional and conventional reservoirs. For unconventional-resource plays, a Markov chain Monte Carlo (MCMC) machine-learning technique is used to create more-objective production forecasts.

In contrast with traditional-practice methods to create production forecasts, which typically use a best-fit (error-minimization) method, the MCMC approach accounts for the inherent uncertainty of data. It can provide a calibrated family of forecasts that will have a probability of exceeding some amount of total production volume. Likewise, for conventional reservoirs, uncertainty of physical properties of the reservoir and of the hydrocarbon fluid is expressed through probability distributions in combination with correlation between parameters and dependency between reservoir targets.

Project Modeling and Economic Evaluation. When uncertainty of production forecasting, expressed through probabilistic assessments, is incorporated into economic models, options that may be taken during the course of execution of a project are illuminated. Application of Bayesian statistics and the Monte Carlo method to full project simulations enables evaluation of information gained by the activities of a project, such as drilling of exploration and appraisal wells. During project simulation, new information is acquired as events proceed, and a revised estimate of performance is created. 

The realized option path within any Monte Carlo iteration is determined not necessarily by whether a key project component is above or below a certain threshold (decision criteria) but rather by how far the project component is from the threshold and how much the component must change to clear the threshold. A relation can be made to the confidence of being above or below the threshold. Doing so requires probabilistic estimates rather than the more commonly used discrete estimates. Sensitivity analysis is now embedded within the project model, allowing determination of the important factors that truly affect the outcome of a project. Finally, calculation of expected economic value is performed across the identified spectrum of outcomes. 

A constant challenge in project characterization is modeling to the appropriate level of granularity. A model with too many details, many of which might be unresolved at the time a funding ­decision must be made, often provides no additional insight into a course of ­action (lack of clarity). 

At the portfolio level, projects that are characterized with too much granularity may actually lead to an optimization scenario composed of unrealizable goals. The reliance on the portfolio optimization to inform the specific manner of execution, such as which specific wells to drill and in what sequence, in contrast with allowing the BUs to construct development scenarios for a “project,” will often lead to disappointment. Instead, the ideal granularity for project characterization is the level that captures the constraints and dependencies that, after optimization of the portfolio, can be matched by the more-granular annual budgeting process.

Specifically, the optimization will tend to schedule the most valuable projects first. However, there may be execution constraints. This complexity can be extremely difficult to describe to the linear optimization algorithm in the necessary terms of mathematical functions, but might be very easy to include in a “program” of wells that are manually scheduled in the project model. Framing characterizations with a focus on the funding decisions guides the project model toward the appropriate level of granularity. 

Portfolio Management and Decision Making. The corporate portfolio of the E&P company mentioned in the opening of this paper consists of an exhaustive inventory of all projects available for investment. To identify the suite of selections that maximize the value of the corporation, commercial linear/nonlinear optimization software is used. 

The systems that support data flow to the portfolio are just as important as the portfolio itself. Once all projects have been loaded into the port­folio database, and constraints have been described within the portfolio model, optimization may proceed. 

Portfolio optimization (linear optimization) is the tool that ensures that a given solution lies on the efficient frontier of all possible project selections. The efficient frontier defines the maximized expected value for any portfolio of capital projects as a function of variance. Although some schools of finance often refer to the concept of risk vs. reward, the use of risk in this sense actually refers to uncertainty. In this context, the authors label the concept as variance. Greater variance might, and often does, lead to greater risk because of a greater cumulative probability of outcomes that lie below the threshold of commerciality, but risk and variance are distinct concepts. This is especially so when project selections contain embedded decisions that act to minimize the magnitude of loss of an unsuccessful project. 

It is natural to distinguish the relationship of value vs. variance by decision stage. Development opportunities are the investment decisions with the least uncertainty and the most granularity. An exploration opportunity may be broken down into many discrete development opportunities as it is moved through the decision framework and the major uncertainties are resolved. 

It follows that portfolios weighted toward opportunities that lie in a specific stage would capture the relationship of value vs. variance for that stage. Optimization ensures that the project selection lies on the efficient frontier and will result in a selection of capital projects that achieves maximum value. Once the model is set up, the linear-optimization software evaluates tens of thousands of possible realizations of the corporate portfolio, in minutes, to identify the highest-value suite of opportunities. 

Solutions (People and Process)

Organizational Alignment and Resources. A successful alignment shifts strategy development and project-­funding decisions to the corporate center, while regional BUs execute the projects that make up the long-range corporate strategy. The corporate center is then able to optimize capital efficiency across the entire set of investment opportunities in the pursuit of many possible goals. The challenge is to strike the right balance; the corporate center must be able to generate scenarios and provide funding guidance out of the portfolio, while the BUs must provide the long-term characterization of projects for optimization as well as execute projects chosen for funding. 

The E&P company’s implementation of a portfolio process was undertaken as a sequence of initiatives with clear deliverables focused on building critical capabilities and infrastructure within key groups, while driving organizational alignment around the process. Major steps in the change-management effort included the following.

Portfolio-Modeling Group. A centralized portfolio-modeling group was established, consisting of a small team of technical experts reporting directly to the executive leadership team through a sponsoring executive. 

Project Modeling and Cash-Flow Characterizations. A companywide initiative was undertaken to characterize the cash-flow potential for every prospective opportunity in which the E&P company holds an ownership interest. 

Software and Systems Investments. Investments were made in commercial-software applications for project modeling, portfolio optimization, and database infrastructure. 

Organizational Alignment. Exhaustive efforts were undertaken by the portfolio-modeling team and the executive sponsor to drive alignment of multiple corporate and regional BU groups around the business processes necessary to sustain a robust portfolio process for investment decision making.

Executive Adoption. As the capabilities grew within the organization and the efficacy of the methods was demonstrated, the executive group increasingly embraced the process, ultimately adopting these methods and tools as the central planning and investment decision process for the corporation.

Discussion

By formalizing toolsets and application of best practices for each element, confidence in the entire strategic planning process is increased. Confidence in the underlying data increases as well, and work that once took weeks is now completed in hours. The entire company can be optimized, analyzing a scenario through several iterations, in a matter of half a day.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 187162, “People and Process: Integration of Technology and Organizational Alignment for Successful Implementation of a Strategic Capital Investment Portfolio,” by David S. Fulford, Gregory P. Starley, and Michael Berry, Apache; Derrick W. Turk, Terminus Data Science; and James R. DuBois, 3esi-Enersight, prepared for the 2017 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8–11 October. The paper has not been peer reviewed.

Integrating Technology and Organizational Alignment for a Portfolio Implementation

01 December 2017

Volume: 69 | Issue: 12

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