A New Method for Injectivity Impairment Characterization From Well and Coreflood Data

Abstract

The injector impairment due to suspended particles during waterflood project happens in two stages: firstly particles penetrate into a reservoir and are captured by the rock (deep bed filtration); at the second stage, after the reservoir inlet is plugged by particles, the particles build up a filter cake. Prediction of injectivity decline is based on mathematical modelling of deep bed filtration and filter cake formation. The model parameters are determined from either laboratory tests or field data. The model parameters for deep bed filtration - the filtration and formation damage coefficients - can be determined from laboratory pressure measurements using so-called 3-point pressure method. The method for determination of the critical porosity fraction, which is necessary for calculation of the time of transition from deep bed filtration to filter cake buildup, is not available in the literature. In the current paper, the 3-point pressure method was used for determination of the critical porosity fraction. The data from 18 laboratory tests were treated, and the correlation between the formation damage coefficient and the critical porosity fraction was obtained. This correlation allows determining of the deep bed filtration and filter cake buildup parameters from either routine coreflood test or well injectivity history. Introduction Injection well impairment during sea/produced water flooding is a widespread problem in offshore field development. It happens due to solid and liquid particles, which are present in the injected water and are captured by rock, resulting drastic permeability decline1,2. It resulting in drastic increase of operation costs. Impairment of injectors during poor quality water injection occurs in two stages: first the particles penetrate into the reservoir making formation damage (so called deep bed filtration), at the second stage the particles form an external cake after plugging the inlet cross section3–5. The injectivity decline prediction is performed using mathematical models of deep bed filtration3,4,6 and external cake formation3–5. The data for reservoir formation damage simulation are obtained from laboratory coreflood tests. The mathematical model for deep bed filtration contains two empirical constants - filtration and formation damage coefficients. These parameters can be determined from measurements of the particle outlet concentration and pressure drop during coreflooding3,4,6. The concentration measurements are cumbersome and complex while the pressure measurement is a routine laboratory procedure. Measurements of pressure in some intermediate core point together with its measurements at core inlet and effluent (so-called 3-point pressure method) allows determination of both filtration and formation damage coefficients7,8. The transition time of switching from deep bed filtration to external cake formation is determined by a critical porosity fraction, which should be filled by deposited particles in order to prevent further particle penetration into porous rock. Due to lack of method for critical porosity fraction measurement, it is proposed to use the value 0,53,4. In the current paper, a method for laboratory determination of the critical porosity fraction is developed. The method uses pressure measurements in three core points. The data of 18 laboratory corefloods were treated, and the model parameters were obtained. Analysis of the results allows for correlating the formation damage coefficient with the critical porosity fraction. This correlation allows for complete characterisation of deep bed filtration and filter cake buildup systems from either routine coreflood or well injectivity history. Analytical Model for Deep Bed Filtration and External Cake Formation Fig. 1 shows two stage of injectivity impairment: deep bed filtration described by eq. (A-1)-(A-11) from Appendix A, and external filter cake formation modelled by (D-1)-(D-4).

Stabilization of External Filter Cake by Colloidal Forces in a “Well–Reservoir” System

“…6,7,46 By definition of the transition time t Dtr , the deposited concentration at the transition time is equal to the αth fraction of porosity, resulting in the following expression for transition time: The value α = 0.5 was used in work 8; laboratory tests suggest α = 0.09. 11,16 The thickness of the incompressible cake is proportional to the amount of injected particles after the transition time [8][9][10][11][12]47,48 which also corresponds to the linear impedance growth where k is the reservoir permeability, k rwor is relative permeability for water at the presence of residual oil, k c is the permeability of external filter cake, and ϕ c is the cake porosity.…”

“…[1][2][3]8,10,12 An analytical model was used for prediction of the injection well behavior and for characterization of the coupled deep bed filtration and cake formation system from well injection history. [8][9][10][11][12]16,17 Starting from the moment of significant plugging of the sandface by the captured particles, where the nonplugged pores do not form an infinite cluster anymore, the particles do not penetrate into the formation, and the particles accumulation on the well wall surface in compacted low permeable external filter cake occurs. 1,3,8−12 The assumption of cake incompressibility yields a linear impedance growth versus the amount of injected particles.…”

Upscaling of Stochastic Micro Model for Suspension Transport in Porous Media

“…Usually α varies between 0.03 and 0.4 (da Silva et al 2004) while injected concentration varies between 0.1 and 1,000 ppm. So, the ratio ε = c 0 /α is often significantly less than unity, ε 1, and may be considered in (87) as a small parameter.…”

A New Comprehensive Approach for Predicting Injectivity Decline during Waterflooding

“…The comprehensive approach can capture the initial injectivity decline due to the deep bed filtration, the faster decline due to the external cake formation, and the equilibrium stage due to the external cake erosion and the rat hole filling. Most of the model parameters are taken from experimental measurements, while others can be from the empirical correlations, such as the correlation between the critical ratio for external cake formation and the formation damage coefficient (Silva et al, 2004), and the estimation of hydrodynamic corrector from cake properties (Sherwood, 1988). Few parameters that are not easily available can be estimated by fitting the field data, such as the friction coefficient depending on the lever ratio and the erosion factor for the non-ideality of the erosion process.…”