Formation damage in production wells has been less studied than in injection wells. In injection wells, decline in well injectivity can occur, due to fine particles suspended in the water. These particles are deposited in the reservoir, blocking the pores, causing reduction in the permeability of reservoir rock near the well. Thus, the decline is due to injected water quality and reservoir properties.In production wells, particles can be transported through the reservoir and cause formation damage. The damage is caused by deposition near the wellbore as a result of the converging flow geometry. As is briefly discussed, it is a common problem in the water industry, where water production wells suffer from productivity decline due clogging of the aquifer near the wellbore. The accumulation of particles causes plugging of pores and decreases the permeability creating a damaged zone near the well. This results in extra, expensive cleaning operations and early shut-in of producers. This paper reports on an investigation on particle deposition in converging flow geometry, modeling oil and water production wells. Parameters are varied to study the effect on particle deposition of flow rate, particle concentration and particle/grain aspect ratio. The experiments use a converging-flow unconsolidated sand-pack. A hematite particle suspension is injected into the sand pack to observe clogging effects. With the aid of a CT-scanner (X-ray tomography), deposition profiles in time are obtained. Effluent particle concentrations and pressure profiles are also measured in real time. The results of the experiments are interpreted using deep bed filtration theory in converging flow geometry. The experiments show a clear effect of converging flow geometry on particle deposition.

Many oil-field operations involve the injection of fluids into the formation around a well. In many cases, the fluids contain colloidal particles, either initially present or introduced during the operation through dirt or naturally-occurring particles. Therefore, all injection schemes suffer from injectivity decline. This injectivity decline is caused by clogging of the formation by particles, which form an external filter cake on the surface of the formation and block the pores inside the formation. This article reports on a set of crossflow experiments in which a suspension of particles is flowed over the surface of a sandstone core, with a portion of the suspension entering the core itself. An external filtercake is formed on the surface of the core, and internal deposition occurs inside the core. The experiments are conducted in a CT scanner, allowing measurement of the internal deposition in real time. Thus, although the time-dependent build-up of the external filtercake thickness cannot be measured directly, it can be deduced by mass balance, allowing for the crossflow and permeant effluents. This enables correlation of the external and internal mass deposition with the measured increase in injection pressure. We use the cumulative mass curves of the hematite deposited in the cake, core and in the effluent to model the dependence of the pressure drop on cake and core deposition, and compare the predicted pressures with the actual measured pressures. These results are very relevant to operational issues associated with injection or leak-off, including hydraulic fracture propagation, drilling-mud impairment and produced water matrix re-injection. The results are also relevant to water injection under fracturing conditions, since impairment of the wall of the fracture is one of the mechanisms governing fracture growth, and the implications of the results for this process are discussed.

Recently it has been shown that the presence of residual oil in a formation can have a considerable influence on the trapping mechanisms for particles present in re-injected produced water (Ali 2007, Ali et al 2005, 2007). This article reports on a further set of extensive coreflow experiments which confirm and extend these results. The tests were conducted in a CTscanner, allowing direct observation of the build-up of particle deposition along the core.These experiments are relevant to operational issues associated with PWRI (Produced Water Re-Injection). In many cases, produced water is injected into formations containing oil, so reduced oil saturation is achieved rapidly in the area around the well. Even if the well is outside the oil zone, trapped oil droplets are always present in produced water, and a residual oil zone will gradually build-up around the well.Major differences are found between the deposition profiles for fully water saturated cores and the cores having residual oil saturation. In particular, particles penetrate deeper into the core with residual oil saturation and considerably more particles pass completely through the core without being trapped. The X-ray technique allows direct observation during the experiment of the deposition process inside the core, eliminating the complicating effect of any external filter cake. As a result, an analysis can be performed of the deposition parameters relevant inside the core, using Deep-bed Filtration Theory, and the results of this analysis are presented. In particular, it is shown that the values of the filtration function determined from the CT-scan (Xray) data are consistent with those obtained from analysis of the effluent concentration. Moreover, both methods of analysis find quite clearly that the filtration coefficient increases with decreasing flowrate.The results indicate that formation damage near a wellbore during water injection will be reduced by the presence of residual oil, and that particles will penetrate deeper into the formation. The result is also relevant to injection under fracturing conditions, since particle deposition in the wall of the fracture (where residual oil may be present) is one of the mechanisms governing fracture growth.

This work presents the application of a fit-for-purpose history match workflow to a giant and geologically complex carbonate reservoir with over 60 years of production/injection history and 600+ wells. The target was to deliver, within schedule and spec, a high-quality sizeable model (15+ million grid blocks) that honored the underlying geologic characteristics and reproduced the distinctive production mechanisms present across the different regions of the reservoir, while keeping parametrization of uncertainties at a manageable level. Practical implementation routes were applied to efficiently translate key reservoir plumbing elements and other identified subsurface uncertainties into dynamic modeling components that could be investigated over large uncertainty ranges via Assisted History Matching (AHM) tools. To manage the history match process of this vast and mature reservoir, a sophisticated and custom-tailored sector-centered modeling scheme was adopted based on a "Divide & Conquer" approach. This tactic divides the big history match problem into smaller more manageable pieces, allowing for simultaneous history match of different sectors by different engineers while having frequent reassembling of sectors into a full-field model to ensure alignment, preserve consistent reservoir behaviors, and update (flux) boundary conditions. The iterative sector-based history match scheme applied to the giant field dynamic model made it possible to achieve a good history match within the given time and IT resources available to carry out the history match. The new dynamic model respects the conceptual understanding of the reservoir behavior and honors the available subsurface and production data of approximately 80% of the individual wells within the desired history match criteria. The use of the sector modeling workflow approach in a large full field model, allowed for faster turnaround of results for history matching purposes. The applied workflow also demonstrated that achieving a good history match in the individual sectors also resulted in a good history match for the full field model, achieved in a faster way. The final model respects the conceptual understanding of reservoir behavior as well as honors the available performance data at a scale which allows not only more reliable production forecasts but also model-based pattern-level waterflood optimization and its use for well location optimization (WLO) studies. The model supports development planning and reservoir management decisions (20+ new wells drilled annually), with waterflooding aiming to increase ultimate recovery by more than 20%. The methodology allowed significant time-savings to deliver the dynamic model within a relatively short schedule (~9 months) and required quality specifications. The successful application of the custom-made history match workflow is currently being replicated in other reservoirs of similar scale and complexity in North Kuwait and could also be applied to other massive reservoirs around the world. This work also illustrates a good example of achieving excellent HM results while keeping the parametrization of uncertainties as practical as possible.

Extended Abstract This paper presents a fast turnaround Integrated Reservoir Modelling (IRM) workflow for a deep water turbidite field. It is based on a static and dynamic Experiments of Design (DoE or ED) workflow that is used as a toolkit for uncertainty management. The case study shown here emphasizes the application of this "Multi Scenario Approach" methodology for field specific decisions such as short and mid-term reservoir management optimization and infill development opportunities, including impact of key subsurface uncertainties on these decisions. anonumously The objectives of Integrated Reservoir Modelling workflow presented here are: To create fully integrated models or multiple scenarios that provide realistic ranges of forecasts, e.g. geologically sound, to underpin development and operational decisions without anchoring to a single base case; andTo shorten modelling life cycle to achieve faster project delivery and allow for uncertainty workflows as proposed in this paper. There are many decisions that are dependent on model forecasts such as identifying infill well target locations and number of wells at the project level, project phasing to extend production plateau, and to identify and mitigate key projects technical risks