Inferring Reservoir Interwell Dynamic Connectivity Based On Signal Processing Method

Abstract

Abstract A reservoir can be considered as a complete system, which includes injectors, producers and the flow channels between them. Injection rate is a stimulus of the system while produced rate is a response. However, there are time lag and attenuation occurring between stimulus and response, because of the influence of reservoir porous media. Thus, time constant was introduced for quantitatively characterizing the injected signal's time lag and attenuation. Influence law of reservoir static parameters and dynamic parameters to injected signal's time lag and attenuation were studied based on numerical simulation technology, and then, the relationship formula between reservoir pressure transmitting coefficient, well spacing and time constant was obtained. Based on this study, a new mathematic model called SP model for inferring interwell dynamic connectivity was established by method of signal processing and a global optimization solution is obtained on the basis of an improved genetic algorithm. According to this model, injected signals will be preprocessed by use of a convolver before inferring interwell dynamic connectivity. The model was validated through its application to typical synthetic fields, and the result shows that the injection-production system is a first-order linear system, and the constructed signal convolver can accurately describe the dynamic propagation features of injection signals in reservoir porous media and effectively improve the inference effect of interwell dynamic connectivity with a fit precision of production rate of 0.977. Moreover, field application in J2X4 reservoir of wellblock Lu-9 in Junggar basin indicates that the interwell connectivity can be divided into four types, including isotropous connectivity, unidirectional connectivity, linear connectivity and single well onrush connectivity. A further statistic study shows that unidirectional and single well onrush connectivity-type waterflooding well groups are dominant, accounting for 46.7% and 30% of total well groups respectively. 1 Introduction Reservoir connectivity is not only the important content of reservoir evaluation but also the basement of successful oilfield development and management program. As the development of reservoir, especially in later water flooding stage, the water-oil flow law in porous materials becomes more complicated. At the same time, the phenomenon of injection water breakthrough in single direction is usually found. Therefore static state connectivity can't reflect formation property and reservoir fluid connectivity correctly. Knowledge of interwell dynamic connectivity and finding out injection water flow direction, can not only guide the optimization measures of injection profile modification operations but also helpful to quantitatively describe remaining oil distribution in high water cut stage. At present, the commonly used methods of evaluating reservoir interwell dynamic connectivity are tracer test, pressure test, well testing and numerical simulation. But these methods are difficult to carry out, need spending huge property and affect normal production. Therefore many researchers put forward the method of inferring interwall connectivity combined with the dynamic injection-production data and geological statistic. Because the injection and production rates are easy to obtain, inferring the interwell dynamic connectivity based on injection-production data becomes an important method (Jansen, and Kelkar, 1997; Heffer et al.,1997; De Sant'Anna Pizarro, 1998; Soeriawinata and Kelkar, 1999; Yousef et al., 2009). Albertoni and Lake (2003) made a representative study on inferring interwell connectivity. They built a multivariate linear regression model (MLR and BMLR) with injection-production rate and obtained the weighting coefficients, which quantitatively characterize the reservoir dynamic connectivity.