A New Approach of Infill Drilling Optimization for Efficient Transition to Future Pattern Flood Development

Abstract

Infill drilling has been recognized as a common practice to accelerate oil production and increase ultimate recovery. Infill drilling can be performed under different drive mechanisms (primary, secondary and tertiary). With a certain history of development, many oil fields have become mature to some extend with waterflood. In order to have a sustainable corporate development plan, pattern flood towards further EOR is considered. Nonetheless a tertiary process as a whole project involves massive investment with high risks and uncertainties. If incremental oil can be recovered via infill drilling as a transition, the investment can be partially offset and justified. Infill oil producers as components of pattern flooding can be accelerated while pattern water injectors can be scheduled in a latter phase. Two main approaches are used in the determination of infill potential. The first one uses empirical techniques to determine infill wells number and spacing based on volumetric calculation of oil in place. It ignores impact of reservoir heterogeneity and continuity. The second approach relies on numerical simulation coupled with optimization algorithms. Based on the second approach, this paper presents a new one that looks at the remaining mobile oil distribution at the time of infill drilling, and locates the optimum pattern configurations whose centers have the maximum sum of stacked mobile oil thickness of each pattern. Each square pattern has only one oil producer centered without corner water injectors. An automated algorithm has been generated to identify infill potential and locations. First, the remaining stacked mobile oil distribution is calculated; second, multiple average-spacing pattern realizations are placed on the field, and only one realization is chosen since it has the highest value of summing mobile stacked oil thickness; third, remove infill wells which have nearby existing oil producers in the pattern area; then, select perforation intervals with a certain criteria to avoid early water/gas breakthrough; after that, an automatic schedule of infill wells is output for simulation run to screen potential infill wells having minimum impact on the existing wells. This infill drilling approach identifies potential pattern oil producers to recover mobile oil, sustain the production plateau and increase oil recovery, prior to planning pattern water injectors. In offshore field, tower slots are limited, so some infill wells can be utilized to workover/sidetrack future inactive wells to save slots. Infill wells can be coupled utilizing conventional completion strategy to minimize wells count. These wells act as a smooth transition to future pattern configurations towards further EOR to recover remaining oil. For the first time, this paper demonstrates a novel approach of determining infill locations by chasing in-situ stacked mobile oil thickness at the specified time step. An automated program is generated to efficiently identify infill wells at any time step. A complete workflow of infill drilling and transition to pattern flood is prepared for a full image. This process also suits both new and mature field. Pattern flood is accelerated by drilling infill oil producers and followed by water injectors.

Incremental and acceleration production estimation and their effect on optimization of well infill locations in tight gas reservoirs

“…In this method, decline curve analysis is used to forecast the Estimated Ultimate Recovery (EUR). Utilizing this EUR per well and total hydrocarbon in place, one can predict the required number of wells, and consequently, their spacing to drain the hydrocarbon reservoir (Huang and Arii 2016). This approach seems to be relatively simple and can only be used when the production data with are radially available, but the fact that decline curve analysis (DCA) is mainly based on curve fitting, and has no physical basis, makes the results obtained based upon DCA is often questionable (Gaskari et al 2006;McCain et al 1993) and misleading.…”