A key barrier to optimal field management, i.e., maximizing oil production and reducing operational cost, is the understanding of underlying structure of the field, which continuously changes over time. Analyzing readily available injection and production data to identify injector-producer relationships (IPRs) offers a convenient way to this understanding. The capacitance-resistive model (CRM) provides an intuitive and straightforward way to characterize IPRs through production and injection rate fluctuations. However, it requires a large number of model parameters. The number of parameters increases quadratically with number of production and injection wells in the reservoir. For fields consisting of hundreds of wells identifying IPRs among the wells is challenging. Moreover, there is no analytical solution for solving the parameter values due to the nonlinear time constant parameters of the CRM and the constraints. This paper presents a new method, a hybrid constrained nonlinear optimization (HCNO), for estimating the optimal parameter values of a nonlinear predictive model. HCNO is optimization-based algorithm such that it estimates the optimal values of the model parameters satisfying the constraints. HCNO separates the connectivity and time constant parameters of CRM then uses two different optimization algorithms. A constrained nonlinear optimization algorithm is applied to estimating the time constant parameters, and subsequently the connectivity parameters are estimated by a constrained linear optimization algorithm with the estimated time constant parameters. The coupled optimization is performed at each iteration, which leads to faster convergence. HCNO is tested on several synthetic oil fields. The result showed that the search time and the prediction error by HCNO were significantly less than those of estimating the parameters as a whole by solely constrained nonlinear optimization. The identification of IPRs by the optimal parameter estimation will help field engineers’ decision making process in optimizing waterflooding.
Even though the solutions of numerical reservoir simulation are pressures, production rates and fluid saturations; rarely are the fluid-saturations/fluid-contacts included in the history-matching process. History matching to all the available pressures, production rates and fluid saturations/fluid-contacts should increase reliability of a simulation model for forecasting. This history-matching concept was applied to a matured waterflood reservoir (H). The H reservoir was discovered in 1970. It is vertically divided into two main zones (HA and HC).The reservoir started production in 1971 and has been under peripheral water injection since 1984. Nine producers and one injector have been completed in the reservoir. Currently only two producers (HC-104 and HA-111) and one injector (HAC-53) are active in the reservoir with a current recovery factor at 51%. The H reservoir was history matched to the following observed data set: RFT pressure (4 wells), static well pressure (9 wells), flowing wellhead pressure (2 current producers), allocated well production (9 wells), and Fluid-Contacts/Fluid-Saturations (8 open- hole logs). History matching of the flowing wellhead pressure, which was done using flowtables, helped to resolve the gas-lift injection volume in well HA-111. The simulation study was initially done without rigorous attempt to match historical fluid contacts from open-hole logs. Even though reasonable production/pressure match were obtained in some of the wells, the model produced excessive water in well HA-30 and could not achieve water breakthrough in well HC-104. The simulation was then improved by actively history matching the fluid-saturation/fluid-contacts from historical open-hole well logs. Good history match was obtained for pressures, production and fluid-saturation/fluid-contacts in the wells. This resulted in the identification of one new drill opportunity and three workover opportunities with a potential to increase the estimated recovery factor to 64%.
Agbami Field is a deepwater producing asset off the coast of the Niger Delta region in Nigeria. The production system comprises of 26 subsea production wells, 8 subsea production manifolds, subsea flowlines and subsea risers respectively. The production wells are intelligent completions made up of 22 dual zone well completions and 8 single zone well completions. Dual zone well completions are equipped with Interval Control Valves (ICVs) for zonal control & isolation. They are also equipped with several pressure/temperature (P/T) gauges from the sand-face completion to topsides for real time measurement. Production commenced in 2008 and peak production was achieved a year later in 2009. Field production remained at peak for about 8 years. The stellar field performances have been achieved through several reservoir management best practices. These include, but not limited to, robust initial field development strategy, infill development, acid stimulation operations, Real Time Reservoir Management (RTRM) and production optimization using Intelligent Well Completion (IWC). This paper demonstrates the development of an automated system which performs Production Network Model calibration and assessment of production optimization opportunities. The key considerations for automated calibration and optimization system workflow development will be presented. Since deployment, the automated calibration and optimization system has reduced the manhours spent with manual model calibration and optimization assessment by 85%. Lessons learned during the development and deployment of the automated system as well as production gains realized from the solution will be highlighted.
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