The time spent on drilling operations in the development of a field is a major percentage of the costs involved in the production of hydrocarbons. However, one of the major challenges facing drilling and re-entry operations today is the prevention of costs and schedule overruns. This is because of the nature of drilling and re-entry operations, the risks involved with the different tasks from well conception up to the completion is not properly accounted for. Other factors like the failure of a new technology planned to be deployed, the pressure of time and the increasing regulatory of safety and environment requirements have made it difficult achieving the financial and timing targets set. The shortfalls in well cost estimation and control are due to reliance on outdated or poor methodologies.
Generally, drilling AFE's (Authorization for Expenditure) are generated by applying deterministic methods to offset data and, which primarily consists of the recorded and documented experience of the drilling operations. From the analysis of these data, estimates for drilling performance and the likelihood of facing drilling complications are estimated. Added to all these are also an element of expert judgment, though subjective, that is drawn upon in the estimation of time and costs for the new well/s to be drilled or re-entered.
These estimates suffer from the lack of a true scientific approach to predict time and costs which becomes apparent immediately there is a deviation from the planned operations.
There has been limited published work involving the applications of probabilistic methods in drilling engineering, particularly when estimating times and costs for re-entry and drilling operations. This fact is even more acute when considering the evaluation of re-entry opportunities and new drilling technologies. There is little or no scope to incorporate an estimation of risks and non-productive time (NPT) in a meaningful manner.
This paper presents an accurate probabilistic and risk analysis approach using a subsea re-entry well as a case study. An intervention operation was planned and this method was used to accurately predict the amount of days (and cost) required for the operation, factoring in all the possible risks and time impact on the operation. A deviation of less than 5% was observed when the actual expended time and cost was compared to the planned.