The following exercise envisions the use of a hypothetical CAS based tool, called POGO, in the optimization of traditional oil and gas production maintenance schedules.

TARGET CUSOMER SCENARIO EXERCISE
“POGO”
(Production Oil and Gas Optimizer)

User: Oil and Gas Production Company Field Operations
Technical Buyer: IT Department, Field Engineering
Economic Buyer: VP, Production

Before:

Situation: Production management has to integrate production and maintenance activities, within the constraints of the capabilities of the production field equipment, the personnel available and the production profiles of the reserves, to create an operational schedule that produces the best net economic and operational outcome for the production period.

Desired Outcome: Production schedules are set within the practical constraints of formation, personnel and equipment with all maintenance interruptions anticipated and a buffer held in reserve for contingency so that economic expectations are met.

Attempted Approach: Starting with last months schedule and “this month last years” schedule, operations management calls for input from all departments as to which activities must be integrated into the schedule. Using a variety of manual and automated procedures, production management iterates potential solutions until a schedule is created that is the best compromise of all desired activities. Operations management then publishes the schedule to all concerned.

Interfering Factors: Information on production equipment, personnel, formations and planned activities is in a variety of different formats, levels of detail, and levels of accuracy. It is difficult to anticipate the impact of a single desired activity, impossible to understand the net combined impact of hundreds of interrelated activities over the entire production period. Once the production period begins, events begin occurring immediately which interfere with the production schedule and require adjustments to the activities being performed. Understanding the impact of tactical events, and the timely assimilation of those net changes into the necessary modifications to the published production schedule is an uncertain process, full of time delay and the potential to further impact the schedule through misinterpretation and miscommunication.

Economic Consequences: Due to the limitations of the data available, the planning process and the ability to respond to circumstances during the production period, production results are sub-optimized in terms of the potential production rates and the economic results of the assets.

After:

New Approach: POGO is an integrated software environment that connects world class scheduling optimization science to the data architectures that support the field operations and maintenance communities of a major oil and gas production company. Able to see all aspects of the scheduling problem, POGO iterates scheduling options, in dialogue with production management personnel, until the best net economic schedule is arrived at, given all initial inputs. Once the scheduled production period begins, POGO is able to quickly assimilate all unanticipated events into the production schedule and issue adjustments to the schedule.

Enabling Factors: Integrated data access is the enabling technology of POGO. The ability to see all contributing factors, to understand and catalog the nature and relationships of all factors is a key component of the system. Multi-objective optimization is the enabling science of POGO. Using new science which has come out of the study of natural systems, POGO is able to “search the landscape” of solutions to the problem in new and dramatically more effective ways than in previous generations of optimization software.

Economic Rewards: The first benefit of POGO is an initial schedule that is superior to previous efforts by virtue of the systems ability to see all components and to solve for the optimum solution with superior techniques. This initial advantage is overshadowed during the scheduled production period, however, by the systems ability to quickly assimilate changes in the schedule, react to the consequences of the changes, and issue new directives to adjust for the changes. It is a particular property of the new science to be most effective in a networked landscape of solutions where there are many and frequent interruptions to the scheduled events, just such an environment as an oil and gas production schedule. The system solves for production efficiency as well as economic result, so the net solution at the end of the scheduled period is superior in both categories.