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Results
Abstract The drilling of an oil well is a very expensive operation. Consequently, asset teams often seek to minimize drilling costs by optimizing the drilling process. The most important factor in cost consideration is the length of time during which a drilling rig is hired. The fact that the rigs are often hired on a day rate means that the longer the drilling time, the higher the drilling cost. Thus drillers often seek to minimize the total time required to drill a well in order to minimize the drilling cost. However, the drilling time is often made of many components, some controllable and others uncontrollable. While the most common factor drillers seek to minimize is the time spent on the actual mechanical drilling of the well, this is not necessarily the most time consuming part of the drilling operations. Minimizing the time spent on the actual drilling is done by seeking optimum parameters that increase the rate of penetration of the bit. Another factor that may help reduce the total rig time is reducing the frequency at which the bottomhole assembly (BHA) is pulled out of hole to change worn out bits. Thus, it is important to optimize the drilling process to yield the lowest overall drilling time. In this study, we proposed an optimization scheme that optimizes drilling operations by maximizing the drilling rate (ROP) and the bit life. The scheme optimizes drilling operations by finding the optimum values of drilling parameters that reduce the overall drilling time of any particular section of the formation. The drilling parameters we considered are RPM, WOB and mud flow rate. We used differential evolution as an optimizer to obtain the optimum ROP and optimum bit life that yield the lowest drilling time. We set upper and lower bounds for each of the operational parameters and tested the method on different sections. Two cases are considered. The first maximizes the drilling rate (ROP) while the second minimizes the total time (i.e. mechanical drilling time and trip time and bit change time at the surface). Findings show that optimizing the ROP yields the lowest time at shallow depths. However, at deeper depths, optimizing the total time yields better the lowest overall time. Significantly, the approach helps to identify the optimal values of drilling parameters at different hole sections needed to achieve the lowest overall drilling time. Hence, the method can save money that would otherwise have been used to hire the drilling rig for an extended period of time.
Abstract Well placement optimization plays an essential role in reservoir management. When wells are placed in optimum positions in the reservoir and operated at optimum rates or bottomhole pressures, the financial return on investment is expected to be high. In a highly heterogeneous reservoir, placing the wells in appropriate positions, although very challenging, can improve the profitability of investment significantly. Also, operating the wells under appropriate controls can enhance the viability of the project. One challenge with well placement, however, is how to simultaneously optimize well locations, well controls, well types, well schedules and project life cycle. Many works addressing the issue of well placement optimization often fix the project life and also assume that the wells were drilled at the beginning of the project. It is well known that all wells cannot be drilled all at the beginning of the project due to manpower and facility constraints, even if that scenario is the optimal choice for high net present value of the project. In this work, we present an optimization framework that simultaneously finds the optimal number of wells, their types, locations, controls, well schedules and the optimal project life cycle. The method is an extension of our previous work in which all the above except well schedules and project life cycle have been determined using global optimization strategy. We included an additional variable that represents the project life in the list of optimization parameters and one variable per well to represent the fractional time at which the well is to be drilled, completed and put on production or injection. This fractional time is a fraction of the total project life and each well has its own fractional time. This means that wells will not be drilled all at the same time during the project life. To ensure that too many wells are not scheduled for drilling/commissioning during the same year, we adopted the penalty approach to enforce a set of inequality constraints that ensure that the number of wells drilled and put on operation in any particular year is not more than a predetermined number. Differential Evolution was used as the global optimizer to solve the problem. Results show that the method is able to yield high net present value corresponding to the estimated set of parameters including well schedules and project life cycle.
- Europe (1.00)
- North America > United States > Texas (0.46)
- Asia > Middle East > Saudi Arabia (0.28)