Inferring Interwell Connectivity from Well Bottomhole-Pressure Fluctuations in Waterfloods

Abstract

Summary This paper presents a new procedure to determine interwell connectivity in a reservoir on the basis of fluctuations of bottomhole pressure of both injectors and producers in a waterflood. The method uses a constrained multivariate linear-regression (MLR) analysis to obtain information about permeability trends, channels, and barriers. Previous authors applied the same analysis to injection and production rates to infer connectivity between wells. In order to obtain good results, however, they applied various diffusivity filters to the flow-rate data to account for the time lags and the attenuation. This was a tedious process that requires subjective judgment. Shut-in periods in the data, usually unavoidable when a large number of data points were used, created significant errors in the results and were often eliminated from the analysis. This new method yielded better results compared with the results obtained when production data were used. Its advantages include:no diffusivity filters needed for the analysis,minimal number of data points required to obtain good results,and flexible plan to collect data because all constraints can be controlled at the surface. The new procedure was tested by use of a numerical reservoir simulator. Thus, different cases were run on two fields, one with five injectors and four producers and the other with 25 injectors and 16 producers. For a large waterflood system, multiple wells are present and most of them are active at the same time. In this case, pulse tests or interference tests between two wells are difficult to conduct because the signal can be distorted by other active wells in the reservoir. In the proposed method, interwell connectivity can be obtained quantitatively from multiwell pressure fluctuations without running interference tests. Introduction Well testing is a common and important tool of reservoir characterization. Many well-testing methods have been developed in order to obtain various reservoir properties. Interference tests and pulse tests are used to quantify communication between wells. These methods are often applied to two wells such that one well sending the signals (by changing flow rates) and the other is receiving them (Lee et al. 2003). For a large field such as a waterflood system, however, multiple wells are present, and most of them are active at the same time. In that case, pulse tests or interference tests between two wells are difficult to conduct because the signal can be distorted by other active wells in the reservoir. In this method, data can be obtained from multiwell pressure tests that resemble interference tests. Thus, we can have several wells sending signals and the others receiving the signals at the same time. The wells that are receiving the signal, however, can either be shut in or kept at constant producing rates. The pressures at all wells are recorded simultaneously within a constant time interval. The length of the test will depend on the length of the time interval and the number of data points. Results of this method can be used to optimize operations and economics and enhance oil recovery of existing waterfloods by changing well patterns, changing injection rates, recompletion of wells, and infill drilling. This work is based on previous work conducted by Albertoni and Lake (2003) by use of injection and production rates. In their work, Albertoni and Lake developed and tested different approaches by use of constrained MLR analysis with a numerical simulator and then applied it to a waterflooded field in Argentina. They used diffusivity filters to account for the time lag and attenuation of the data. In his thesis, Dinh (2003) verified the method by use of a different reservoir simulator and applied it to a waterflooded field in Nowata, Oklahoma. He also investigated the effect of shut-in periods and vertical distances on the results. The main objectives of this work are to verify the results obtained from pressure data with results from flow-rate data to propose a new method to determine interwell connectivity and to suggest further research and study on the method. Similar to the method that uses production rates, we will concentrate on a waterflood system only. The reservoir is considered as a system that processes a stimulus (i.e., a well that is sending signals) and returns a response (i.e., a well that is receiving the signals). The effect of the reservoir on the input signal will depend on the location and the orientation of each stimulus/response pair. Because the total pressure changes at active and observation wells are not equal, only the MLR (Albertoni and Lake 2003; Dinh 2003; Albertoni 2002) was used. The effect of diffusion was not significant, thus the diffusivity filters were not used. The method was applied to two synthetic fields, one with five injectors and four producers and the other with 25 injectors and 16 producers.

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

“…The MLR model was a precursor for the CRM presented by Yousef et al [7]. Analogously to the Albertoni and Lake [40] model, Dinh and Tiab [41] used only BHP fluctuation data to estimate interwell connectivities via an MLR, without requiring diffusivity filters.…”

Use of connection constraints for checking and enhancing geological models

“…Connections can be identified during the Basic Reservoir Engineering phase of a reservoir study, for example by analyzing well tests, pressure and fluid production data in neighbor wells and interference tests. Many authors have worked on this issue and there is actually a great variety of methods for detecting the presence hydraulic connections between wells, which are based on various kinds of measurements such as flow rate fluctuations, pressure perturbations or multiwell productivity (Albertoni and Lake, 2003;Yousef et al, 2006;Tiab and Dinh, 2008;Kaviani et al, 2010;and Soroush et al, 2014). These methods will not be detailed here and the uncertainty on interwell connectivity inference will not be addressed.…”

An integrated procedure for reservoir connectivity study between neighboring fields

“…However, updating the simulation model using the new data through History Matching process is time-consuming. Therefore, mostly a direct relationship between injector and producers is built to integrate the flow rate, BHP or both data to evaluate inter-well connectivity (Albertoni and Lake 2003;Yousef et al 2006;Tiab and Dinh 2013;Salehian and Cinar 2019). Similarity of the trends of the pressure changes in two reservoirs during production is a sign of the connectivity of the two reservoirs.…”