Analysis of multi-layered commingled and compartmentalized gas reservoirs

Abstract

The evaluation and performance prediction of multi-layered compartmentalized gas systems can be difficult. This is mostly due to the uncertainties related to production allocation either within each commingled well or between interrelated reservoir compartments. This paper presents a model that can provide reliable estimates of the total gas in place for multi-layered commingled and compartmentalized reservoirs. The model is also capable of generating prediction profiles for every well in the production system in addition to forecasting individual layers production for each compartment. The proposed model is based on coupling the layered stabilized flow model for material balance calculation in commingled systems with communicating reservoir model that is used as material balance tool for compartmentalized gas reservoirs. The model has the flexibility to be applied for history matching and prediction purposes. In history matching, the model solves the equations simultaneously using optimization routine to find the best parameters of original gas in place (OGIP), deliverability coefficients and compartment transmissibility coefficients. The model requires the knowledge of initial reservoir pressure in every compartment, some rate production history and bottom-hole flowing pressures. The model can also utilize additional information such as shut-in pressures per layer, repeat formation tester and production logging tool measurements (if available) to improve the history match. For prediction, the model uses the estimated parameters (compartment OGIP, transmissibility coefficients between compartments and flow parameters for each layer) to calculate the production rates and reservoir pressures for every well/tank based on a provided bottom-hole flowing pressure. The model was verified against a commercial reservoir simulator for several synthetic cases. The model was also applied on different field cases to estimate OGIP and flow coefficients for every layer as well as compartment transmissibility coefficients. Moreover, calculation of cumulative gas transferred across the communicating reservoirs allows detection of poorly drained compartments, which could be included in future redevelopment plans.

Calculation Model of Drainage Radius of Single-Layer/Multi-Layer Commingled Gas Production Wells in a Closed Constant-Volume Gas Reservoir and Its Application

“…2696 × 10 8 πr 2 ej h j ϕ j S gij /T ij (12) where P ij is the original reservoir pressure of layer j, MPa; Z ij is the deviation factor corresponding to P ij ; T ij is the temperature of the reservoir, K; G pj is the cumulative gas production of layer j, 10 8 m 3 ; G ij is the controlled reserves of layer j, 10 8 m 3 ; S gij is the initial gas saturation of layer j; ϕ j is the porosity of layer j. Substitute Equation ( 12) into Equation ( 11):…”

“…Bela et al [11] extended the DTM method to the horizontal wellbore model in order to obtain individual layer permeabilities and skin factors of stratified reservoirs with multilateral wells. Sallam and El-Banbi [12] and Juell and Whitson [13] attempted to analyze multi-layered commingled gas reservoirs using an iterative workflow. Although they achieved history in the matching of gas wells or gas reservoirs through continuous iteration, these methods have multiple solutions.…”

“…However, the above models could only compute the average drainage radius for the gas well with a single layer [2][3][4][5][6][7][8]. Although some studies [9][10][11][12][13] have involved multi-layer commingled gas production wells, a clear calculation formula of the drainage radius of these wells was not proposed. In addition, although the well test data required for calculation of the drainage radius can be obtained by using the pressure drawdown test and pressure buildup test, its accuracy from pressure drawdown test is easily affected by many factors, while the long shut-in time required for pressure buildup test can seriously impact the production of gas field and the economic feasibility of gas reservoir development [14][15][16][17].…”

Study of the Seepage Mechanism in Thick Heterogeneous Gas Reservoirs

“…Conventional analytical solutions cannot accurately characterize heterogeneity nor seepage [23][24][25][26]. Moreover, many experiments have shown that there are mechanisms such as high-speed non-Darcy seepage, stress sensitivity, movable water flow, and aqueous-phase nonlinear percolation in tight gas reservoirs, which cannot be accurately described by traditional analytical solutions [27,28]. Therefore, this paper takes Block A in the Xihu Sag of the East China Sea Basin as the research object.…”

A Novel Dynamic Splitting Method for Production Based on Material Balance Theory and Catastrophe Theory in Tight Gas Reservoirs

“…Researchers have proposed many new production splitting methods on the basis of various mathematical theories and basic seepage laws [20][21][22][23][24]. Hu et al used grey relational analysis to analyze the various weights that affect the split of production [25].…”