Horizontal Well Calibration Using Zone Log Matching and Machine Learning

Abstract

Placing horizontal wells in the correct zones of the producing reservoir in static/dynamic models is important for robust model quality and essential for production history matching. A laborious technique of manually generating correction points around each un-calibrated well is often used by geomodelers. This technique is not systematic and is highly interpretive. We present an automated global solution that leverages not only the well tops, but also the well trajectory information to calibrate the horizontal wells. The solution comprises a series of processes that can all be run through a custom built graphical user interface (GUI). The processes are designed to a) detect the calibration problems with the ability to visualize them, b) correct each surface separately for zone mismatch and c) retrieve original zone thickness whenever possible. Treating each surface separately simplifies the problem and causes great reduction in run time compared to simultaneous correction of all surfaces in one go. However, the final results are essentially global, which is insured by maintaining the input zone thicknesses (except if well data confirms otherwise). The correction is achieved by ensuring that model zones (between the input surfaces) match the "optimum zone logs" at all wells in the model. The term optimum, in this context, means that the zone log should only contain the zones to be modeled, no more no less. These optimum zone logs are typically calculated from the well tops, which implies having an impeccable set of well tops. In most cases, well tops sets may contain extra tops (that are not included in the model) and missing tops at some of the wells. While the latter problem is more difficult to solve, both problems must be detected and corrected for a successful run of this solution. Fortunately, the "diagnosis" step at the beginning of this solution detects all these problems, which reduces the time needed to review the well correlation, especially in fields with hundreds of wells. Moreover, machine learning techniques are used to suggest the position of the missing well tops. The user gets to either approve the suggestion or change it manually. The solution we are presenting is fully automatic and fully reproducible. It is given with four parameters to control the amount and influence of the correction applied to the horizon intersecting the trajectory when it is not supposed to. If optimum zone logs are ready, the solution can run in few minutes to correct large models with hundreds of wells and tens of surfaces. This has the potential of reducing months of work to few days to calibrate horizontal wells in a structural model. The few days may include optimization of the solution parameters to achieve the desired results.