What if ...?

We recently benchmarked our Actenum Rig Activity Scheduling (RAS) application against a manually-derived operational rig schedule obtained from one of our clients, a large oil producer. We achieved a 6% reduction in rig transportation costs and a 38% reduction in time to production, which translated into more than a 15% increase in net present value.

Impressive, tangible results. Not surprisingly, however, there was a trade-off between schedule cost and production—improving one objective is, to some extent, detrimental to the other. Fortunately, RAS has automated features for exploring this trade-off.

The metric we use for production is called “production delay”. A low value for production delay is good, because it means less idle time and early drilling of high volume wells (a more detailed definition is provided at the end of this post). In the figure below, we have placed production delay along the horizontal axis, rig cost along the vertical axis, and the client's rig schedule (which we have called the “Manual Schedule”) at the 100% mark on both axes.

When you work on a schedule in RAS, it displays a measure of schedule quality using a variety of metrics. You can experiment with changes to the schedule until the metrics improve. When we make a sequence of changes to the “Manual Schedule” we obtain a series of new schedules with different combinations of cost and production delay relative to the starting point. Clearly, we do not want to end up in the upper right quadrant with higher costs and delay. But, what about the lower right quadrant? It might make sense to increase delay somewhat to achieve lower costs.

Manually evaluating different scenarios and trade-offs can be quite time consuming. But Actenum RAS improves productivity by automating schedule comparison and trade-off analysis.

Which schedules are achievable and how are the trade-offs made? To answer this question, RAS generates trade-off plots. See below for an example of multiple scenarios compared to the original “Manual Schedule”.

For simplicity, we have only shown the manual schedule point and a few others (for how these points were selected, see the notes below). The plot produces a “frontier” of good, feasible schedules (representing different trade-offs).

The plot shows that you can improve production delay and rig cost simultaneously. The points PC, CP, and 5PC are better than the manual schedule for both objectives. Costs have been reduced up to 5% and production delay has been improved up to 40%. In this example, the reduction in rig cost when using 5PC represents an $11.4 million saving.

The plot shows that you can reduce the rig cost further, with 5%, at the cost of almost a 50% increase in production delay. And vice versa, it shows you that you can reduce production delay further by increasing your rig cost. 40PC provides the lowest production delay. The production delay at this point is 38% less than the manual schedule and the rig cost has increased by only 11%.

The figure below shows the production rates for the manual schedule and 40PC over a 36 month period. The integral of these curves represents the cumulative production. As expected, the optimized schedule provides not only a higher cumulative production, but also higher flow rates earlier in time.

Moving production forward in time has a positive effect on Net Present Value. The NPVs relative to the manual schedule for all the scenarios in the trade-off curve are shown in the figure below

In the real world, trade offs are common. The challenge is to evaluate the effects of trade off scenarios on key performance metrics in complex scheduling environments. Actenum RAS supports decision making by simplifying analysis of trade-offs. This benefits users through increased production and lower costs, resulting in higher project NPV.

Notes

• For more detail, see the paper that I presented at the SPE's 2008 Annual Technical Conference and Exhibition in Denver, Colorado. Since then, we have released Version 3.0 of Actenum RAS.
• The “production delay” metric measures the difference between the accumulated oil production of (a) our schedule and (b) a hypothetical schedule using enough rigs to drill all wells on the first day.
• In the trade-off plots above, we only included a few scenarios. These points are said to be Pareto-efficient. A point along the frontier is considered to be Pareto-efficient if there is no other point that is better in both objectives.