Optimization of Multicycle Steam Stimulation
Authors: Hong K.C. , Jensen R.B. 
Published: December 2007

Abstract
The problem of determining the optimum set of steam volumes and cycle lengths for a single well undergoing multicycle steam stimulation in order to maximize the cumulative discounted net income has been formulated mathematically and programmed for a digital computer. The mathematical formulation of the problem and the method for its solution are discussed in this paper.
The oil production performance during each stimulation cycle was simulated by either a constant percentage or a harmonic decline. Using simple analytical expressions for production performance, the cumulative discounted net income performance, the cumulative discounted net income and cumulative time of operation were related to the pertinent process and cost parameters and two pertinent process and cost parameters and two principal process control variables (cycle length principal process control variables (cycle length and steam injection volume).
The discrete maximum principle was used to transform the equations for cumulative time of operation and cumulative discounted net income into a set of simultaneous equations. The simultaneous equations then were solved by trial and error on a digital computer to determine the set of cycle lengths and steam injection volumes that gives maximum cumulative discounted net income over the project life.

Introduction
Steam stimulation is a process for improving the oil recovery rate from wells producing high-viscosity crudes. The process is applied on an individual well basis and is executed in a series of cycles, each consisting of three phases: steam injection, soaking (steam condensation), and production.
Significant increases in production rate following the stimulation operation result from heating the reservoir around the wellbore. As heat is removed with produced fluids and dissipated into nonproductive formations, the production rate declines, usually to near the prestimulation value. Typical production responses are given in case histories reported by Owens and Suter, and are depicted in Fig. 1. Duration of the production phase is equal to the time for the oil production rate to decline to some specified value and is called the cycle length. (Cycle length is defined as the producing portion of the cycle and does not include producing portion of the cycle and does not include the downtime required for steam injection and soaking operations.) Termination of the production phase coincides with the start of steaming for the phase coincides with the start of steaming for the next cycle. The process is continued, cycle by cycle, until it becomes unprofitable.
Models have been developed to simulate behavior of a single well during one cycle of steam stimulation and have been used to investigate the effects of system and operating conditions on the production responses. However, these investigations production responses. However, these investigations have not shown how the models can be used to determine a most profitable set of operating conditions for a multicycle stimulation project. Perhaps these models are too complicated Perhaps these models are too complicated mathematically to be adapted readily to an optimization study. This paper presents a method for optimizing a multicycle steam stimulation project based on simple production performance project based on simple production performance models.
First, the performance of a steam-stimulated well during each cycle was simulated by either a constant percentage or a harmonic decline model. Using these simple analytical expressions for production performance, the cumulative discounted production performance, the cumulative discounted net income (before tax) and cumulative time of operation for each cycle were related to the pertinent process and cost parameters and two pertinent process and cost parameters and two process control variables-the cycle length and the process control variables-the cycle length and the steam injection volume. Finally, the profit function to be maximized was formed.
The analogy between the above problem and problems of optimizing multisage derision problems of optimizing multisage derision processes that have been reported extensively in processes that have been reported extensively in the chemical engineering literatures in recent years has led this investigation to the use of the discrete maximum principle. This principle was used to transform the equations for cumulative discounted net income and cumulative time of operation into a set of simultaneous equations characterizing the optimum operating conditions in terms of the two control variables.