Assessing 4D, New Wonder Tool, Or More Work Needed?

Abstract

Abstract 4D is established in some parts of industry and there are some spectacular technical successes reported, mostly in monitoring water floods. There is still much to improve in how we use 4D to capture its potential value and in the tool itself to make it faster, better, cheaper and / or more sensitive to small production effects. Introduction There are two aspects to improving the value of 4D. We should make better use of the 4D information we do obtain and also improve this information how we obtain it. If we are using 4D properly to track production changes in the subsurface then Asset Teams should be calmly going about their business, seeing what is happening in their fields, anticipating problems and planning to optimize recovery. There should be no surprises and much less firefighting. Instead we find surprises to be the norm for 4D, usually requiring immediate remedial action. The information is often coming to the asset too late. Also, 4D is a look back and we need to be able to use it better for prediction and forward planning optimization. In some quarters 4D is still considered an expensive add on to production plans. Where 4D is an add on, then there will likely be no provision to take advantage of the new information, no provision for injection change, adjusting the intake rates along a well, sidetracking etc. We need to get all disciplines together towards planning and treating field production as an uncertain but manageable adaptive process. We should use 4D together with other sensors to track field status, and use this to update reservoir models and improve predictions and optimization plans. We should also improve how we acquire and analyze 4D data. When 4D is treated as a series of 3D surveys then the time elapsed between deciding on the need for a monitor survey, and the completion of planning, contracting, shooting processing and interpretation is just too long. If field managers are used to having to react quickly to surprises coming from real time information like pressure and production rates then a field manager will have to think hard about being enthusiastic over a new approach which gives field status that was current 6 months to a year ago. Our traditional 3D approaches turn out to deliver unrepeatable data unless special acquisition and processing are used. The sensitivity of 4D depends upon data repeatability. The better data repeats then the smaller the reservoir changes we can diagnose, which means we can diagnose production related changes and their deviation from prediction earlier and thus have greater impact upon field management. The earlier we can see fluid front movements deviating from expectation then the earlier we can take action to control the flow to perhaps reduce bypassed reserves and improve sweep. The earlier we can detect effects due to compartmentalization and identify the location of pressure boundaries then the earlier we can influence the positioning of new wells or sidetracks, or other remedial action. Our experience also shows that the 4D pressure depletion effects are generally considerably less than we would expect so we need more sensitivity.