bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation and Optimisation of Chemically Enhanced Oil Recovery in Fractured Reservoirs Using Dual-Porosity Models
Authors: Al-Rudaini Ali , Geiger Sebastian , Mackay Eric , Maier Christine , Pola Jackson 
Published: April 2019

Abstract
We propose a workflow to optimise the configuration of multiple interacting continua (MINC) models and overcome the limitations of the classical dual-porosity model when simulating chemically enhanced oil recovery processes. Our new approach captures the evolution of the concentration front inside the matrix, which is key to design a more effective chemically enhanced oil recovery projects in naturally fractured reservoirs. Our workflow is intuitive and based on the simple concept that fine-scale single-porosity models capture fracture-matrix interaction accurately and can hence be easily applied in a commercial reservoir simulator. Results from the fine-scale single-porosity system are translated into an equivalent MINC method that yields more accurate results than the classical dual-porosity model or a MINC method where the shells are arbitrarily selected.
Our approach does not require the tuning of capillary pressure curves ("pseudoisation"), diffusion coefficients, MINC shells, or the generation of recovery type curves, all of which have been suggested in the past to model more complex recovery processes. A careful examination of the fine-scale single-porosity model ("reference case") shows that a number of nested shells emerge, describing the advance of the concentration and saturation fronts inside the matrix. The number of shells is related to the required degree of refinement, i.e. the number of shells, in the improved MINC model. Using the results from a fine-scale single-porosity simulation to set up the shells in the MINC model is easy and requires only simple volume calculations. It is hence independent of the chosen simulator.
Our improved MINC method yields significantly more accurate results compared to a classical dual-porosity model, a MINC method with equally sized shells, or a MINC model with arbitrarily refined shells for a number of recovery scenarios that cover a range of matrix wettabilities and permeabilities. In general, improved results can be obtained when selecting five or fewer shells in the MINC. However, the actual number of shells is case-specific. The largest improvement is observed for cases when the matrix permeability is low.
The novelty of our approach is the easy-to-use method to define shells for a MINC model to predict chemically enhanced oil recovery from naturally fractured reservoirs more accurately, especially in cases where the matrix has low permeability. Hence the improved MINC method is particularly suitable to model chemical EOR processes in (tight) fractured carbonates.
Novel Method For Mitigating Injectivity Issues During Polymer Flooding at High Salinity Conditions
Authors: Schmidt Julia , Mirzaie Yegane Mohsen , Dugonjic-Bilic Fatima , Gerlach Benjamin , Zitha Pacelli 
Published: April 2019

Abstract
Synthetic high molecular weight polymers have been utilized for enhanced oil recovery applications. Improving their injectivity remains an important issue for field applications. Large entangled polymer chains can clog pore throats, leading to injectivity decline. We investigated an emulsion polymer system and have developed a series of processing techniques to condition an acrylamide-based copolymer inverse emulsion system at a salinity of 50,000 ppm TDS before injection into porous media. The investigated polymer solution contained 4,000 ppm active emulsion polymer and 2,400 ppm inverter surfactant.
The un-conditioned polymer system and test conditions were chosen to clearly demonstrate the impact of processing techniques on the injectivity behavior. The polymer solution was sheared with two agitators, a disperser and Ultra-Turrax, at different intensities and with a pressure-driven flow into a thin capillary to reduce the size of the largest polymer chains and disentangle the polymer chains while maintaining its viscosifying power. The injectivity of such differently sheared solutions was evaluated by performing filtration tests using a 1-micron membrane and sand-pack flooding tests.
Our experiments have established a master curve showing viscosity and screen factor dependences on accumulated energy during pre-shearing, regardless of the mode of shearing. The un-sheared polymer solution had an unfavorable behavior in filtration test and sand-pack flooding experiment. After pre-shearing, the filtration behavior of polymer solution and the injectivity in sand-packs improved significantly. Polymer solutions sheared with a disperser at an energy input of 15 MJ/m3 improved the injectivity gradient (e.g. the ratio of the resistance factor over 30 pore volumes injected) from 3.7 to 1.6, while the viscosifying power was reduced by only 2%. To reach the same injectivity improvement with Ultra-Turrax, an energy input of 31 MJ/m3 were required, which reduced the viscosity by 11%. Shearing the solution using a capillary at an energy input of 50 MJ/m3, did not reduce the injectivity gradient while viscosity was reduced by 19%. This indicates that the injectivity performance is shear-origin dependent and the resulting polymer structure, when sheared through contractions, has a different alignment as compared to shearing with the agitators, the disperser and Ultra-Turrax.
Optimizing Well Spacing and Fracture Design Using Advanced Multi-Stage Fracture Modeling and Discrete Fractured Reservoir Simulation in Tight Oil Reservoir
Authors: Tian Changbing , Lei Zhengdong , Jiang Qingping , Chang Tianquan , Chen Dongliang , Lu Zhiyuan , Li Sheng 
Published: April 2019

Abstract
Large platforms, long horizontal sections, small well spacings and dense cutting have become economical and effective development means for tight oil reservoirs. Well spacing and fracture design are critical parameters impacting production and Internal rate of return (IRR) of tight oil reservoirs. In order to maximize the total stimulated reservoir area and fracture-controlled reserves, the well spacing and fracture spacing should be small enough. However, in order to minimize the chance of fracture hits caused by offset wells and the overlapping drainage area of a nearby well to avoid Asset spillover, the spacing well should large enough.
Based on minifrac data and microseismic fracture mapping results, a natural/hydraulic fracture network was generated and input into an unstructured-grid-based discrete fracture reservoir simulation model. Its accuracy was calibrated with the well production history. For each group of fracture design and well spacing, well interference was determined by estimating ultimate recovery (EUR) difference between a single well and a middle well among multiple wells. Based on actual information of tight oil developments, the pressure interference were examined by field trail data and well spacing simulations. The real scenarios were selected to study effects of well spacing on EUR and ultimate IRR. Effects of reservoir permeability and fracture half-length on optimal well spacing were also analyzed.
It was found that the decrease in Long-term EURs for different well spacings is a good indicator for well spacing optimization. Based on the reservoir simulation and economic analysis, the maximum IRR of the tight oil reservoir with permeability of 0.23mD can achieved when the well spacing is 260m. Meanwhile, the detailed results were also illustrated to show the effects of fracture half-length, reservoir permeability as well as oil price variation on IRR.
The paper demonstrates an effective method and a workflow to optimize well spacing and fracture treatments design through integrating advanced multi-stage fracture modeling with discrete fracture reservoir simulation in the area of unconventional resource developments. Such optimization studies contribute to minimize operation cost and improve the economy of resource development.
New Geosteering Technology Boosts Wellbore Data and Seismic Properties Integration: A Case History From Norway
Authors: Cossa Alessandro , Manassero Elena , Mele Maurizio , Musca Claudio , Tarchiani Cristiano , Arrigoni Veronica , Halset Gjertrud 
Published: April 2019

Abstract
In a deep-water green field's development, horizontal wells are drilled to exploit the reservoir with the aim of reducing drilling cost and time to first oil.
During geosteering operations, Ultra Deep Azimuthal Electro-Magnetic (E.M.) measurements permit investigating the reservoir around the borehole up to a maximum depth of 30 m, in a good resistivity contrast environment. Acquired data are inverted on a vertical section, providing multi-boundary reservoir mapping along well path.
With such a depth of investigation, the reservoir mapping is an excellent bridge between conventional logging-while-drilling (LWD) and seismic images. The integration of acquired wellbore data with high-resolution attributes, from seismic inversion, maximizes well placement results when operating in complex subsurface geology and expands the perspectives of geosteering application.
A workflow to calibrate the reservoir structural and stratigraphyc setting has been assessed, via integration of seismic and Borehole Data. Enhancement of reservoir geometry interpretation during geosteering provides revised structural surfaces suitable for a quick update of the velocity model and a depth-calibration of all the seismic attributes used to steer wells.
We describe an application of the workflow to an infill well, targeting channel and crevasse splays deposits drilled through a structurally complex oil field in the Norwegian offshore. The availability of seismic attributes (probabilities of facies and petrophysical properties) allowed improving the overall results of the well placement operation.
Reservoir mapping identifies in real time Geo-bodies crossed by the well and within the range of investigation of Ultra Deep E.M. tool based on tool configuration, frequencies analysed and resistivity contrasts of the rocks. Stratigraphic correlation with offset wells, using conventional LWD data supported by Image Log interpretation, allowed allocating resistivity boundaries in terms of stratigraphic surfaces. These data are then integrated in near real time to depth calibrate maps, update the velocity model, hence the depth image of seismic attributes.
After depth calibration, Geo-bodies recognized on seismic show a good correspondence with those identified on the resistivity inversion and a detailed correlation of the heterogeneous fluvial sand was possible, even in presence of minor faults. In this challenging structural and stratigraphic environment, the correlation supported decision making during well operations to target the well on the pay sand.
The application proves that a detailed stratigraphic interpretation is an achievable goal in real time to steer successfully the well and to be used afterwards in a detailed reservoir model update.
Application of Algebraic Multigrid in Fully Implicit Massive Reservoir Simulations
Authors: Kayum Suha , Cancelliere Michel , Rogowski Marcin , Al-Zawawi Ahmed 
Published: April 2019

Abstract
Algebraic Multigrid (AMG) methods have proven to be efficient when numerically solving elliptic Partial Differential Equations (PDE). In reservoir simulation, AMG is used together with the Constrained Pressure Residual (CPR) method to solve a partially decoupled pressure system. Recently, effort has been focused on improving the robustness of the AMG-CPR solver. This paper presents the performance of different AMG-CPR strategies for massive reservoir models. In addition, a solver selection analysis is conducted, proving that dynamic selection of solvers has the potential of increasing the overall efficiency and robustness of the simulation.
Numerous decoupling/preconditioning algorithms exist and have been shown to influence the pressure matrix properties, some resulting in matrices more suitable to the characteristics favorable to AMG. Several decoupling/preconditioning strategies are investigated, such as Alternate Block Factorization (ABF), Quasi-IMPES (QI), and Dynamic Rowsum (DRS). The extracted pressure matrix could be suitable or unsuitable for AMG, depending on the matrix row sum, the diagonal signs, and the signs of the off-diagonal values.
The advantage of using AMG as a preconditioner is demonstrated by running the SPE10 case. The recommended AMG settings that result in the optimal performance for SPE10 are shared. A speedup is seen of up to 4X when using AMG with optimal settings versus the default solver in the in-house reservoir simulator with the improvement range depending on the number of processors used. SPE10 is a highly heterogeneous model resulting in matrices favorable for AMG, i.e., pressure decoupling produces positive definite pressure matrices, which is not necessarily representative of industry models. A comparison is then made with a selection of models with a wide range of characteristics and finally an examination of the convergence behavior of key industry cases with different decoupling strategies is presented. The overall convergence behavior of the pressure and full system are shown and the top decoupling algorithms for the particular models are discussed. Finally, the applicability and performance gain of selectively using AMG during a run is demonstrated.
Recent developments have been made in regard to AMG methods, but their applicability in a wide range of massive real cases is yet to be explored. In this work, different decoupling methods are tested, the AMG behavior on real field massive models is analyzed, the scalability is investigated, and AMG is selectively activated during a simulation run shedding light on the potential of future work entailing the use of Artificial Intelligence (AI) to dynamically select the optimal solver choice.
Waterflooding Management and Optimization for Reservoir Simulation with Coupled Geomechanics and Dynamic Fractures Using Streamline-Based Information
Authors: Lei Zhengdong , Xie Qichao , Tao Zhen , He YouAn , Zhu Zhouyuan , Peng Yan , Liu Canhua 
Published: April 2019

Abstract
Waterflooding of fractured low permeability reservoirs are often associated with poor sweep and high water cut due to existence of natural fractures, hydraulic fractures, and artificially induced fractures. Therefore, reservoir simulation with coupled geomechanics and dynamic fractures is required for this application. In this work, we present the use of streamline-derived flux information to improve overall waterflooding performance in such complex simulation problems.
This work shows the waterflooding optimization workflow of a fractured low-permeability reservoir in ChangQing Oilfield, China. First, the finite difference simulator considering stress field and geomechanical properties is used to simulate the growth of dynamic fractures. Then, the newly formed fracture properties are included into the dual porosity/permeability reservoir simulation model. Afterwards, streamlines can be traced based on the velocity field of this model, which represent a snapshot of the inter-well fluxes. Finally, with the goal of minimizing field water production, we implement linear programming algorithms to optimize the waterflooding operation by considering the inter-well connectivity and well allocation factors.
Through reservoir simulation coupled with geomechanics, we have found that induced fracture growth rate is relatively limited at reasonable injection rate, which is also validated by field empirical observations. This can avoid fracture propagation and reduce the risk of rapid water breakthrough. We deploy our streamline tracing and linear programming based optimization program to work together with this simulation model. A controlled and cautious increase in injection rate has resulted in a positive production response in 28 producers in the pilot area. Reallocation of water to high-efficiency injectors improves sweep efficiency in the reservoir. Finally, the optimized scenario has resulted in more than 15% incremental swept volume as compared to the basic development case.
This work provides a comprehensive case study for better understanding the impact fracture growth on waterflooding performance in fractured low-permeability reservoirs. It further establishes the workflow of using streamline-based flux information for oil production optimizations in these complex simulation problems.
Assessment of Enhanced Oil Recovery and CO2 Storage Capacity Using Machine Learning and Optimization Framework
Authors: You Junyu , Ampomah William , Kutsienyo Eusebius Junior , Sun Qian , Balch Robert Scott , Aggrey Wilberforce Nkrumah , Cather Martha 
Published: April 2019

Abstract
This paper presents an optimization methodology on field-scale numerical compositional simulations of CO2 storage and production performance in the Pennsylvanian Upper Morrow sandstone reservoir in the Farnsworth Unit (FWU), Ochiltree County, Texas. This work develops an improved framework that combines hybridized machine learning algorithms for reduced order modeling and optimization techniques to co-optimize field performance and CO2 storage.
The model's framework incorporates geological, geophysical, and engineering data. We calibrated the model with the performance history of an active CO2 flood data to attain a successful history matched model. Uncertain parameters such as reservoir rock properties and relative permeability exponents were adjusted to incorporate potential changes in wettability in our history matched model.
To optimize the objective function which incorporates parameters such as oil recovery factor, CO2 storage and net present value, a proxy model was generated with hybridized multi-layer and radial basis function (RBF) Neural Network methods. To obtain a reliable and robust proxy, the proxy underwent a series of training and calibration runs, an iterative process, until the proxy model reached the specified validation criteria. Once an accepted proxy was realized, hybrid evolutionary and machine learning optimization algorithms were utilized to attain an optimum solution for pre-defined objective function. The uncertain variables and/or control variables used for the optimization study included, gas oil ratio, water alternating gas (WAG) cycle, production rates, bottom hole pressure of producers and injectors. CO2 purchased volume, and recycled gas volume in addition to placement of new infill wells were also considered in the modelling process.
The results from the sensitivity analysis reflect impacts of the control variables on the optimum results. The predictive study suggests that it is possible to develop a robust machine learning optimization algorithm that is reliable for optimizing a developmental strategy to maximize both oil production and storage of CO2 in aqueous-gaseous-mineral phases within the FWU.
Development and Application of an Artificial Neural Network Tool for Chemical EOR Field Implementations
Authors: AlAbdullah Mohammad , Emami-Meybodi Hamid , Ertekin Turgay 
Published: April 2019

Abstract
The field-scale design of chemical enhanced oil recovery (cEOR) processes requires running complex numerical models that are computationally demanding. This paper provides an efficient screening platform for the cEOR feasibility study by presenting five artificial neural network (ANN) based models. We constructed 1,100 ANN training cases using CMG-STARS to capture the variation in reservoir petrophysical properties and the range of injected chemicals properties for a five-spot pattern. The design parameters were coupled with the reservoir properties using several functional links to optimize the ANN models and improve their performances. The training cases were employed using back-propagation methods to construct one forward model (Model #1) and four inverse models. Model #1 predicts reservoir response (i.e., oil rate, water cut, injector bottomhole pressure, cumulative oil) for known reservoir characteristics (i.e., permeability, thickness, residual oil saturation, chemical adsorption) and project design parameters (i.e., pattern size, chemical slug size and concentration), Model #2 predicts reservoir characteristics by history matching the reservoir response, and Model #3 predicts project design parameters for known reservoir response and characteristics. Models #4 and #5 predict project design parameters for a targeted cumulative oil volume and project duration time, which is useful for economical evaluation before the implementation of cEOR projects.
The validation results show that the developed ANN-based models closely predict the numerical results. In addition, the models are able to reduce the computational time by four orders of magnitude, which is significant considering the complexity of cEOR modeling and the need for reliable and efficient tools in building cEOR feasibility studies. In terms of accuracy, Model #1 has a prediction error of 5% whereas the error for other four inverse ANN models is about 20–40%. To enhance the performance of the inverse ANN models, we changed the ANN structure, increased training cases, and used functional links, which slightly reduced the error. Further, we introduced a back-check loop that uses the predicted parameters from the inverse ANN models as inputs in the forward ANN model. A comparison of back-check results for the reservoir response with the numerical results delivers a relatively small error of 10%, revealing the non-uniqueness of solutions obtained from the inverse ANN models.