Drilling is ultimately a capital allocation process. Once financial capital has been set aside or reserved to construct a well, those funds are not available for other projects. But does this matter today where good projects are on hold not for want of financial assets, but because of equipment, material and human shortages? The answer is yes, asset utilization always matters regardless of the level of activity in the industry.

Maximizing economic utility

Pareto Optimality is defined as “the best that could be achieved without disadvantaging at least one group.” Oil and gas field operations involve integrating a number of geoscience, engineering, economic, financial and managerial criteria into the decision-making process. The economic utility or overall level of satisfaction (value) attained in this decision-making process is a function of the optimization of all variables, not just the maximization of one or a few, across the economic Efficient Set or frontier. In this scenario, no single variable in the decision-making process is maximized, but overall, expected return for the project is optimal. This construct is well defined in the field of economics.

Drilling solution

During well construction, formal risk assessment is the beginning of a life-of-the-well decision support process that incorporates a large amount of structured and unstructured data as well as subjective knowledge. Often such a complex process tends to be across organizational and skill set silos among knowledge workers with a wide range
of experiences and responsibilities. Capturing both quantitative and qualitative data necessary to make good decisions has long been difficult, particularly across the entire decision process.

Operators are faced with any number of drilling prospects. Some offer higher returns at greater risk and some are more easily determined. In any given period a company may have several opportunities in its portfolio. The drilling risk assessment process determines the expected value, adjusted for risk, of each prospect and is a tool that enables the operator to maximize investor returns.

However, there is one problem that has only recently been overcome. Many use Monte Carlo simulation to run a statistical model of the opportunity. Typically, these models are cumbersome and are limited in the data sets and subjective input into the model. Moreover, they are expensive.

A new tool has been developed that enables drilling decision makers to input all data, geoscience, engineering, procurement, economic, etc. into a single model whose output is the Bill of Materials as well as the tasks and resources required by project management software tools. Moreover, the output is in accordance with the Project Management Institute’s (PMI) Best Practices. This solution incorporates all components of the drilling risk assessment process.

The process is divided into two segments. The preliminary decision is reached quickly and at low cost. If corporate criteria and hurdle rates are met, the second phase incorporates a more robust decision model that includes procurement, regulatory compliance issues, and greater detailed engineering (often including input from the service companies). At this point the AFE (authorization for expenditure) is released.

In most companies, this formal process is not followed for each well drilled. Typically, expensive, difficult development wells where management faces a number of unknowns and uncertainties are prime targets for this solution.

Process simulation and optimization

How can this process be made more effective and efficient? Using a structural dynamics based inference engine, a documented process model, including feedback, can be modeled enabling engineers, management and geoscientists to manage a large amount of data and variables in a single model. Very robust models with up to a million nodes have been used to solve very complex scenarios, although most applications only require a hundred or so.

The inference engine runs a series of iterations that steer large-variables sets to convergence at a Pareto Optimal solution. A typical inference engine will control the workflow and data input of the overall process. It would include the ability for management to override based on an individual or group of individuals’ knowledge.

Finally, the output feeds to executive dashboards, thus adding a level to field intelligence that typical solutions, including those with Monte Carlo simulation, cannot emulate. This is the beginning of real analytic and decision support power — the Pareto Optimal solution that decision makers from the field to the board room can use to attain competitive advantage!
The output includes a number of scenarios from which management can select implementation plans. For example, one recent simulated scenario saved a refinery almost 18% during an upgrade process. The value is documented, and it is substantial.

This approach towards asset utilization is grounded in economics and capitalizes on proven portfolio management techniques to realize significant value to the firm. This type of solution has been used in other industries with great success and it is now poised to do the same for upstream oil and gas.

This is essentially a capital allocation model. By helping management develop an effective expected cost for each well construction project in the portfolio, operators are better able to manage capital expenditure realizing better overall portfolio perspective. For example, if the expected cost range is narrowed, less capital will have to be allocated for any given project. Likewise, if cost overruns are minimized then that capital can be put to other and better uses.

Traditionally, capital is thought of in financial terms. Today, human, equipment and material capital are in even shorter supply than money. The efficient allocation of these types of capital is at least as important as the financial component.

The promise of digital energy

Integrated operations is in its infancy. Some companies are still short of the proof-of-concept level while others are taking tentative steps beyond. Additionally, Business Intelligence is beginning to take on the mantel of Field Intelligence, recognizing that revenue generation and direct costs are the major variables in any upstream operator.

Current management dashboards are one-dimensional, simply displaying data often in the form of bar and pie charts with an occasionally “gas tank” meter look and feel. This limited information is far short of providing executives with the tools necessary to run the revenue producing asset and the enterprise at optimal levels. Simulation inference engines tasked to specific integrated technical and business processes offer high economic value propositions — realizing the full potential of digital energy.

These tools and construct are forming the basis for true Lean Energy management which is the next great step towards maximizing shareholder value. The promise of lean is not just for large operators; value will be earned at any size firm using sophisticated decision-support models.

One has only to look at other industry sectors that have employed lean techniques such as statistical process control to see who the winners and losers are.

A similar scenario is possible for the upstream sectors as it embarks on the digital path.
Over the next few years, the industry will increasingly have access to specific tools and techniques that will transform the digital energy company of today into the lean energy firm of tomorrow. Sophisticated models like the one described here will directly integrate into mainstream enterprise resource planning systems, with a focus on operations and maintenance.

These solutions will capitalize on the full range of structured and unstructured data as well as the subjective expertise of the multidimensional operator and its supply chain. This will release additional economic value regardless of commodity price points.

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