MANAGEMENT & ECONOMICS: Integrated E&P decision-making involves technical-to-business processes
Adapting to changing market conditions
A typical technical-to-business system implemented to enable process integration at a major oil and gas company.
Upturns and downturns, followed by upturns, are the facts of life in the petro-leum business. Because of these roller coaster business cycles, the industry today is focusing much more on the creation of real value and competitive advantage. Clearly, cost reductions alone are not going to meet share-holders' or stated earnings' expectations.
Today, the ability to adapt to changing market conditions and achieve more efficient returns on capital has become the name of the game. If an oil and gas company can convince the investment community that it is more adept at making investment decisions than its competitors - and can prove it in terms of its higher financial returns - the company will attract more investment capital than its competitors.
In view of the competition to attract market capital, what should an exploration and production (E&P) company's strategy be? One winning strategy might be to stay focused on the core competency of finding and managing hydrocarbon assets, but adopt new technology to improve decision-making.
Risk-based asset evaluation and enterprise portfolio management are integrated to achieve more efficient capital allocation, budgeting, forecasting, and performance management.
Today, software tools can construct rigorous, holistic models of the subsurface that allow decision-makers to evaluate business choices in the relative safety of a virtual world before committing to non-reversible and expensive actions in the real world. "Rigorous" indicates that all inputs have some degree of uncertainty, while "holistic" means that the inputs come from a variety of technical and business processes.
Significant progress has been made in building an integrated decision system for E&P companies so that they can develop their own inventory of opportunities from which they can create their futures. Such a system offers value to E&P companies because it addresses technical and business analyses holistically, incorporating risk and uncertainty at every link in the E&P value chain.
The rigorous, more holistic approach of technical-to-business (T2B) integration reverses the prevailing decision-making paradigm. This tactic improves financial returns because capital is allocated only when the impact on shareholder return on investment or cost reduction is made clear.
Example input parameters in risk-based asset evaluations.
For E&P companies to achieve higher returns through T2B process integration, they need to adopt integrated risk-based, asset evaluation and enterprise portfolio management technology. Such an approach enables managers and corporate planners to better understand all the uncertainties and risks inherent in E&P investment opportunities, from prospects to portfolios. With software that is scaleable from well to field to asset to the corporate level, E&P management and staff no longer have to wrestle information from a broad range of IT systems in their quest to optimize potential investment opportunities.
The "wrestling" that occurs today is because little, if any, integration exists between the infor-mation technologies used for subsurface technical analysis and the spreadsheet-based tools used for business and financial analyses. Rather than disconnected processes with multiple software applications, T2B software offers management decision-support that provides insights about their business that they currently do not have.
The software captures and exploits technical information generated by multidisciplinary teams. This information is then integrated with economic and financial input from corporate planners. The results are optimized based on corporate goals, objectives and constraints.
The critical elements in the technology are:
- The process must have the ability to perform deterministic, decision tree, and Monte Carlo simulation techniques.
- Once individual models are complete, projects must be rolled-up, analyzed, and consolidated into a portfolio. At the portfolio level, complete stochastic roll-ups must be available on an enterprise basis, covering hundreds of wells, reservoirs and projects.
- Results must be stored in a flexible decision support system that provides complete data-mining capabilities into the cash flow, earnings, etc. forecast.
- It must have an optimization algorithm that enables managers to select different efficient portfolios of projects or prospects with varying risk and return components.
Traditionally, E&P companies have not used sophisticated technology to model risk and return on their investments. Many companies manage risk by setting high investment hurdle rates and attempting to reduce decision complexity by using discrete economic models, ranking opportunities by profit-to-investment ratio and funding higher return projects until the capital supply is exhausted.
The results from this approach over the last 10 years have been poor. E&P return-on-assets for the top 24 producers in the US has averaged only about 7%. Achieving higher returns is possible if management and staff alike adopt more powerful tools and processes.
Production decline modeling using initial rate and type of decline.
At the asset level, geoscientists and engineers need to model technical uncertainties in reserves, production, cost and commodity price, and the flexibility to transition from simple, discrete economic models to more powerful probabilistic or stochastic modeling techniques. Risk must be explicitly addressed and understood.
At the enterprise level, planners need to simulate, analyze, monitor, optimize, and communicate strategic technical and financial aspects of the enterprise. Once seamless integration is achieved between the asset and enterprise level, E&P managers can:
- Control an enterprise based on value principles
- Translate corporate strategies to operational targets
- Transition from today's report-based management system, dominated by backwards-looking monthly and quarterly reports from financial systems, to a future-focused, rolling-forecast based performance management system.
Substantial value in implementing a rolling-forecast management system allows organizations to quickly adapt resource allocation and business activities during the planning period or fiscal year to changing market conditions. Nowhere is the need for this flexibility more apparent than when managing investments in deepwater prospects.
Integrating T2B processes began as a technique used in evaluating high-risk, costly opportunities such as deepwater prospects that have significant subsurface and economic risks. Deepwater economic models are often very detailed because a large range of possible outcomes has to be considered. Typically, stochastic modeling is used on these prospects because no other decision analysis tool can adequately cover the complexity of deepwater play analysis.
For deepwater prospects, advanced economic modeling techniques allow the analyst to better identify and evaluate the uncertainties that control oil and gas accumulation, prod-uction and development parameters, commodity prices and other economic factors that affect project profitability. Engineering, economic, fiscal, and geoscience data provided by the respective specialists are modeled at the project level. The integration in this process highlights the interdependencies and economic impact of each team's data.
Once the analysis of individual projects is complete, they are rolled-up to an asset, business unit, or corporate basis. A portfolio optimization process can select projects that meet a variety of corporate strategies, goals and constraints. This methodology clarifies the risk and return parameters of the investment and enables management to see the contribution and risk of deepwater projects in the overall corporate results.
A tight integration of deepwater projects with portfolio modeling processes allows analysts to account for real-time changes affecting investment decisions. Once models are created, a capital allocation tool can easily modify investment strategy as new opportunities arise, prices change, additional geotechnical and economic information becomes available, or as fiscal constraints change.
With this new multidisciplinary approach, all of the geoscience, engineering, and economic data are together in one system. It is very easy for the analyst to move back and forth between data sets, update models, and quickly analyze the effect of those updates. With this integration, there is workflow efficiency and communication efficiency. Geoscientists, engineers, and corporate planners are working together, leading to faster consensus on data issues for the prospects and clearer focus on the key economic drivers.
Price is the biggest driver of all. Typically, high, medium, and low prices are considered and the minimum price environment is determined for the prospect. Some companies have moved to stochastic price modeling and are starting to model deepwater project economics with mean reverting or random walk prices.
The complexity of deepwater economic decision-making merits a more rigorous and holistic approach than that supplied by spreadsheets and simple economic modeling tools. Once the benefits of the approach are acknowledged, the next question is how to get started.
Roadblocks the industry faces in adopting more sophisticated modeling, that require technical and business inputs, involve people, pro-cess, and technology change management issues. People need to be trained to appreciate the power of stochastic modeling. Existing simple economic software systems that run on individual PCs need to be replaced by client-server technology that links well-to-reservoir, field-to-asset, and asset-to-portfolio for the entire opportunity set in a global corporation.
Besides the serious people and technology issues, there are the traditional economic process issues to overcome because the prevailing upstream workflow is to ignore risk and produce the most accurate discrete model possible. In a typical portfolio, most of the models are built with simple or discrete economics. While this deterministic practice may be acceptable for the 70-75% of the mature producing assets being modeled, it is inadequate for the remainder of higher risk projects.
Given this mix, engineers usually prefer simple discrete modeling tools for economics, and planners prefer aggregating results in spreadsheets that add volatility and optimize with elementary general solvers for the small number of higher risk projects modeled. The resulting disparate workflows are so laborious that E & P organizations detest "planning pain." The pain affects management, planning, and geotechnical staff. The pain comes from a variety of sources including:
- Difficult, slow, or non-existent project economics roll-ups
- Inconsistent models from business units and divisions
- Spreadsheets with serious inaccuracies that cannot be linked together
- Little time to analyze data
- Incomplete and inaccurate information
- Top-down capital allocation decisions that cannot be understood by the troops who built the bottom-up models.
Some geoscientists and engineers are so caught up in the gymnastics of their broken processes that they do not even realize how disconnected their systems have become. For that reason, the greatest incentive for change comes from the streamlining of technical workflows when adopting an integrated risk-based asset evaluation and enterprise portfolio management system.
To many corporate planners, portfolio management focuses on exploration and occurs only after the tedious, manual roll-up process. True portfolio optimization requires selecting different efficient portfolios with varying risk and return components. It requires that each project be evaluated based on its contribution to the overall portfolio.
However, portfolio optimization is just one of the many tools used to improve strategic decision-making. Probably 90-95% of the E&P planning process pain can be met with the online analytical processor (OLAP), what some information technology people call "cube tools, info-cubes, or multidimensional schema."
By leveraging OLAP technology, planning analysts can create multiple portfolios using their own strategic input. For example, an analyst can understand what the effects are on the probabilities of achieving 12% ROCE (return on cash employed) after exploration, 125% reserve replacement, $3.50/boe F&D, $4 million in net cash flow per year, 7% growth rate in net cash flow per year, etc. Thanks to the OLAP, these key performance indicators can be understood for any individual project, organizational level or for the entire corporate portfolio. Spreadsheet-based systems do not offer analysts the power to perform these analyses. Other OLAP benefits include:
- A rigorous decision tool based on modern portfolio theory to assure that capital is being deployed most efficiently
- A sophisticated risk management tool that lets users precisely define the limits of risk exposure as they seek maximum returns.
Once the risk-based asset evaluation and enterprise portfolio management system is in place, engineers can evaluate project economics for mature assets and higher risk prospects. Furthermore, planners can quickly assess the likelihood of achieving performance goals, and management will have a better understanding of the volatility of different portfolios and their impact on earnings.