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A long term polymer injectivity trial has been conducted in a medium-high oil viscosity field in Oman, with a suite of objectives to assess the feasibility of the high viscosity polymer concept, evaluate alternative polymers and de-risk the low-salinity polymer hybrid for field implementation. An integrated analysis of this injectivity trial is presented. Performance in the field during injection of low and high molecular weight (MW) polymer in hybrid with low salinity water is analysed along with interpretations of the intermediate water injection, shut-ins, pressure fall-off and step rate test at the end. The analysis is based on simulation models to history match the pressure response in the injector during the polymer trial. Key realizations focusing on two aspects – polymer rheology and wellbore/reservoir fluid propagation mechanism – are presented that were used to history match baseline water injection, low and high MW polymer injection, low salinity water injection and polymer low salinity water hybrid. Other sources of information – step rate test and pressure fall-off tests conducted during the trial – were interpreted to validate the conclusions from the HM model. The study highlighted the risks of either inducing fractures or reactivating an existing fracture network in the reservoir during the polymer injection. Different sources of data indicated fracture pressure lower than anticipated. The dynamic characterization of the fractures – propagation and shrinkage – depending on the injection rate, injection fluid type and viscosity are found critical to explain the entire polymer trial response. The study also highlighted that capturing the polymer rheology adequately in the model, based on laboratory data, helps to explain the trial response. Changing well conformance has been found another important parameter in explaining the trial response. Finally, using the baseline water injection to ensure the permeability field is adequately modelled constitutes another step in understanding the polymer trial.
A long term polymer injectivity trial has been conducted in a medium-high oil viscosity field in Oman, with a suite of objectives to assess the feasibility of the high viscosity polymer concept, evaluate alternative polymers and de-risk the low-salinity polymer hybrid for field implementation. An integrated analysis of this injectivity trial is presented. Performance in the field during injection of low and high molecular weight (MW) polymer in hybrid with low salinity water is analysed along with interpretations of the intermediate water injection, shut-ins, pressure fall-off and step rate test at the end. The analysis is based on simulation models to history match the pressure response in the injector during the polymer trial. Key realizations focusing on two aspects – polymer rheology and wellbore/reservoir fluid propagation mechanism – are presented that were used to history match baseline water injection, low and high MW polymer injection, low salinity water injection and polymer low salinity water hybrid. Other sources of information – step rate test and pressure fall-off tests conducted during the trial – were interpreted to validate the conclusions from the HM model. The study highlighted the risks of either inducing fractures or reactivating an existing fracture network in the reservoir during the polymer injection. Different sources of data indicated fracture pressure lower than anticipated. The dynamic characterization of the fractures – propagation and shrinkage – depending on the injection rate, injection fluid type and viscosity are found critical to explain the entire polymer trial response. The study also highlighted that capturing the polymer rheology adequately in the model, based on laboratory data, helps to explain the trial response. Changing well conformance has been found another important parameter in explaining the trial response. Finally, using the baseline water injection to ensure the permeability field is adequately modelled constitutes another step in understanding the polymer trial.
The availability of ~ 7 years of actual performance data for the ongoing field-scale polymer flood in South of the Sultanate of Oman provides ample opportunities to reveal the reservoir dynamics and its interplay with induced EOR mechanism. The paper focusses on analysis of the polymer pattern behaviour, underlying reasons for such response and key indicators to characterize pattern performance. Upsets in surface polymer injection facility leading to the phenomenon of WAP (Water-Alternating-Polymer) and it's impact on recovery is also assessed in context of actual field examples. The paper then illustrates how this information could be exploited to counter challenges faced in the field, enhance polymer pattern performance, optimize it's further expansion and de-risk any other future EOR development. A nested modelling approach has been employed, wherein models at different scales are generated, tailored to meet the objectives. High resolution 3D conceptual models are built in Shell proprietary tool PolyMoReS to calibrate the model response against the actual polymer pattern behaviour in the field, study the impact of mixing between polymer and water slugs in WAP type of recovery, and affirm the correct polymer rheology. Three segment models covering the field are created and history matched with the use of Stochastic Uncertainty Management. Attempts have been made to obtain history match (HM) on segment, pattern and well levels, with greater emphasis on polymer patterns capturing polymer oil response, water-cut reversals and polymer breakthroughs. Models are then complemented by Pressure Fall-Offs, tracer tests and PLTs to capture uncertainties in fracture growth and areal and vertical conformance. The HM model is then used to predict polymer performance. Significant insights into waterflood and polymer flood performance are gained, which help improve the pattern performances. Assessment of WAP with both conceptual Physics and field segment models demonstrate considerable deferment of oil. Capturing injector – producer connectivity has proven the most pivotal element in explaining polymer oil response and breakthrough. Models indicate that lower than expected incremental recovery and sharper decline of oil response in some patterns are related to the lower polymer mass injected, which in-turn could be attributed to many operational factors (e.g., polymer injection uptime, injection rate, low injection viscosity, WAP), and the presence of natural fractures or uncontrolled growth of induced fractures. The study also reveals optimization opportunity to reduce the volumes of back produced water. The paper presents a comprehensive multi-scale reservoir modelling study for a field with significant historical data of large scale polymer flood. Impact of WAP injection, reflecting the reality of interruptions in polymer flood due to operational upsets, on medium to long term polymer flood value is presented. Analysis of polymer patterns in the field demonstrates how different key indicators e.g., PUF (Polymer Utilization Factor) can characterize pattern performance throughout its life-cycle and answers questions, e.g., why some patterns behaved well, while others not.
We develop a novel ensemble model-maturation method that is based on the Randomized Maximum Likelihood (RML) technique and adjoint-based computation of objective function gradients. The new approach is especially relevant for rich data sets with time-lapse information content. The inversion method that solves the model-maturation problem takes advantage of the adjoint-based computation of objective function gradients for a very large number of model parameters at the cost of a forward and a backward (adjoint) simulation. The inversion algorithm calibrates model parameters to arbitrary types of production data including time-lapse reservoir-pressure traces by use of a weighted and regularized objective function. We have also developed a new and effective multigrid preconditioning protocol for accelerated iterative linear solutions of the adjoint-simulation step for models with multiple levels of local grid refinement. The protocol is based on a geometric multigrid (GMG) preconditioning technique. Within the model-maturation workflow, a machine-learning technique is applied to establish links between the mesh-based inversion results (e.g., permeability-multiplier fields) and geologic modeling parameters inside a static model (e.g., object dimensions, etc.). Our workflow integrates the learnings from inversion back into the static model, and thereby, ensures the geologic consistency of the static model while improving the quality of ensuing dynamic model in terms of honoring production and time-lapse data, and reducing forecast uncertainty. This use of machine learning to post-process the model-maturation outcome effectively converts the conventional continuous-parameter history-matching result into a discrete tomographic inversion result constrained to geological rules encoded in training images. We demonstrate the practical utilization of the adjoint-based model-maturation method on a large time-lapse reservoir-pressure data set using an ensemble of full-field models from a reservoir case study. The model-maturation technique effectively identifies the permeability modification zones that are consistent with alternative geological interpretations and proposes updates to the static model. Upon these updates, the model not only agrees better with the time-lapse reservoir-pressure data but also better honors the tubing-head pressure as well as production logging data. We also provide computational performance indicators that demonstrate the accelerated convergence characteristics of the new iterative linear solver for adjoint equations.
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