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Abstract Injectivity decline of oilfield injection wells is a widespread phenomenon during sea/produced water injection. The decline may result in significant cost increase of the waterflooding project. Reliable modelling-based prediction of injectivity index decrease is important for waterflood design as well as for the choice and preventive treatment of injected water. One of the reasons for well injectivity decline is permeability decrease due to rock matrix plugging by solid/liquid particles suspended in the injected water. The mathematical model for deep bed filtration contains two empirical functions - the filtration coefficient and the formation damage coefficient. We consider the case where both coefficients are linear functions of retained particle concentration. In this case, the model is fully determined by four constants. These constants should be determined from laboratory coreflood tests by forcing water with particles to flow through core samples. A routine laboratory method determines the filtration coefficient from expensive and difficult particle concentration measurements of the core effluent; then the formation damage coefficient is determined from inexpensive and simple pressure drop measurements. An alternative method uses pressure data at an intermediate point of the core, supplementing pressure measurements at the core inlet and outlet. The proposed method furnishes values for the four model parameters. The method is implemented in a simple and robust tool used in field conditions to characterise injectivity damage system and predict injection well behaviour. Introduction Produced water re-injection (PWRI) and seawater injection during waterflood projects may result in drastic injectivity decline. The phenomenon was widely reported for North Sea, Gulf of Mexico and Campos Basin oilfields. One of the main reasons is solid and oily particles which are captured by rock from injected water causing steep permeability decline. The reliable injectivity decline prediction, allowing for injector stimulation planning and for choice of optimal water management strategy, is based on mathematical modelling with well-known values of the model coefficients. The classical mathematical model for deep bed filtration, presented in, contains two empirical functions - the filtration coefficient ? that is equal to particle capture probability per unity length of trajectory, and the formation damage coefficient ร that reflects permeability decrease causing by particle retention. Knowledge of these two parameters is essential for predicting well injectivity decline during sea/produced water injection. Certain existing software packages for predicting well injectivity loss provide the option of adjusting the pressure drop curve by matching both parameters ? and ร, under the implicit assumption that these two parameters can be found from the test. These parameters are empirical; therefore, they must be determined from laboratory coreflood tests by flowing water with particles through the rock. Some works show that both parameters can be inferred from combined measurements of core pressure drop and of suspended particle concentration in the core outlet water. Usually, a coreflood test is accompanied by pressure drop measurements. These measurements are inexpensive and simple to perform, and therefore they are widespread in the literature. Nevertheless, suspended particle concentration data in core outlet water during laboratory tests are hardly available in the literature. This is so because the measurement of concentration data requires special equipment and it is difficult as compared to pressure drop measurements. The above-mentioned difficulties are the motivation for attempting to determine the coefficients ? and ร from pressure measurements only.
- Europe > United Kingdom > North Sea (0.25)
- Europe > Norway > North Sea (0.25)
- Europe > Netherlands > North Sea (0.25)
- (2 more...)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.55)
- Information Technology > Modeling & Simulation (0.54)
- Information Technology > Software (0.34)
A New Method To Characterize Sulfate Scaling Damage From Pressure Measurements
Carageorgos, Themis (UENF-LENEP) | Marotti, Marcelle (LENEP/UENEF) | Bedrikovetsky, Pavel G. (LENEP/UENEF)
Abstract Sulphate scaling with consequent deposit formation and wellbore damage is a well-known phenomenon that occurs during waterflooding, when mixing of incompatible injection and formation waters may result in sulphate salt precipitation and flow restriction. The reliable productivity decline prediction is based on mathematical modelling with well-known model coefficients. The sulphate scaling system contains two governing parameters: the kinetics coefficient characterising the velocity of chemical reaction and the formation damage coefficient showing how the permeability decreases due to salt precipitation. Previous works have derived analytical-model-based method for determination of both coefficients from breakthrough concentration and pressure drop during laboratory coreflood on quasi steady state commingled flow of injected and formation waters, and also from just pressure drop measurements during two corefloods with two different ratios "formation water : seawater". This paper extends the previous works, by sequence of two commingled injections of incompatible waters into the same core with two different ratios "formation water : seawater". Two different slopes of skin factor increase during two injections allow calculating the kinetics and formation damage coefficients in order to predict scaled-up well behaviour. Introduction The Ba / SrSO4 scaling is a chronicle disaster in waterflood projects with incompatible injected and formation waters. Barium sulphate and related scale occurrence is considered a serious potential problem that causes formation damage near the production-well zone. This phenomenon is attributed to precipitation of barium/strontium sulphate from the mixture of both waters and the consequent permeability reduction resulting in loss of well productivity. The chemical incompatibility between the injected seawater, which is high in sulphate ions, and the formation water, which originally contains high concentrations of barium, calcium, and/or strontium ions, may result in disastrous well productivity decline with consequent ecomonic damage to waterflood project. A reliable model capable of predicting such scaling problems may be helpful in planning a waterflood scheme. It may also aid in selection of an effective scale prevention technique through the prediction of scaling tendency, type, and potential severity. A reliable predictive model must use well-known values of the model coefficients. The mathematical model for sulphate scaling contains two phenomenological parameters: the kinetics coefficient from active mass low of chemical reaction showing how fast the reaction and precipitation occurs, and the formation damage coefficient reflecting the permeability decrease due to sulphate salt deposit. Both coefficients are phenomenological parameters depending on rock surface mineralogy, pore space structure, temperature and brine ionic strength. Therefore, they cannot be calculated theoretically for natural reservoirs and must be determined from laboratory corefloods. Reagent and deposition concentration profiles during reactive flows are non-uniform. So, the sulphate damage parameters cannot be directly calculated from laboratory measurements. They must be determined from laboratory coreflood data using solutions of inverse problems. The quasi steady state commingled corefloods by formation water (AF) and seawater (SW) were performed by numerous authors. The kinetics coefficient can be calculated from breakthrough concentration in quasi steady state coreflood with commingled injection of sea- and formation waters. Then the formation damage coefficient can be determined from pressure drop increase during flooding.
- Europe (1.00)
- South America > Brazil (0.68)
- North America > United States > Louisiana (0.46)
- North America > United States > Texas (0.28)
Abstract Severe injectivity decline during seawater injection and produced water re-injection is a serious problem in offshore waterflood projects. The permeability impairment occurs due to capture of particles from injected water by the rock. The reliable modelling-based prediction of injectivity decline is important for the injected-water-treatment design, for injected water management (injection of sea- or produced water, their combinations, water filtering), etc. The classical deep bed filtration model accounts for a single physics mechanism of the particle capture. During laboratory or field data interpretation using this model, it is usually assumed that several simultaneously occuring capture mechanisms are represented in the model by a single overall mechanism. The filtration coefficient as obtained by fitting the model to the laboratory or field data represents the total kinetics of the particle capture. Nevertheless, the kinetics of different capture mechanisms can differ one from another by several orders of magnitude. It highly affects the particle propagation in natural reservoirs and the formation damage. A multiple retention deep bed filtration model describes several simultaneous capture mechanisms. In the present study, a model for deep bed filtration taking into account multiple particle retention mechanisms is discussed. It is proven that the multi capture model can be reduced to a single-capture-mechanism deep bed filtration model. The method for determination of the capture kinetics for all individual capture processes from the breakthrough curve is discussed. As an example, the complete characterisation of filtration with monolayer and multilayer deposition of iron oxide colloids is performed using particle breakthrough curves from coreflooding. Introduction Deep bed filtration of particle suspensions in porous media occurs during sea/produced water injection in oil reservoirs, drilling fluid invasion into reservoir productive zones, sand filtration in gravel packs, fines migration in oil fields, industrial filtering, etc[1,2]. The basic features of the process are particle capture by the porous medium and the consequent permeability reduction[3,4]. Deep bed filtration also occurs in several environmental processes like produced water disposal in aquifers, virus, bacteria and other contaminant propagation in ground water reservoirs, etc[5,6]. Design and planning of the above-mentioned technological and environmental processes should be based on reliable mathematical modelling. The traditional mathematical model for deep bed filtration taking into account advective particle transport and the kinetics of particle retention have been derived essentially following the filtration equation proposed by Iwasaki[7,8]. The model accounts for a single capture mechanism. A number of predictive models have been presented in the literature[9โ12]. The equations allow for various analytical solutions, which have been used for the treatment of laboratory data[13โ17]. Herzig, Leclerc and LeGoff8 present a detailed description of such early work in the review paper. The model has been successfully applied in several injectivity prediction studies[18,19]. Nevertheless, the particles are captured under the reservoir conditions due to different physics forces and mechanisms: size exclusion, surface sorption, electrical forces, sedimentation, diffusion, etc[3,4,6]. Therefore, always appear questions: what is the dominant capture processes? How to account for several capture mechanisms[20,21]? The filtration coefficient in the traditional single capture model is a total of filtration coefficients for all capture mechanisms; it is an effective phenomenological parameter in multi capture environment. The role of multiple simultaneous particle capture processes has long been recognized. Network models account for several mechanisms of particle capture[22โ25]. Numerous capture processes including internal filter cake development have been implemented in the micro scale model Paris[3,4,20,21]. The model allows calculating the retention rate for all the implemented mechanisms.
- North America > United States (1.00)
- Europe (1.00)
- Asia > Middle East > Israel > Mediterranean Sea (0.44)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.88)