Evolutionary multitasking (EMT) is a newly emerging research topic in the community of evolutionary computation, which aims to improve the convergence characteristic across multiple distinct optimization tasks simultaneously by triggering knowledge transfer among them. Unfortunately, most of the existing EMT algorithms are only capable of boosting the optimization performance for homogeneous problems which explicitly share the same (or similar) fitness landscapes. Seldom efforts have been devoted to generalize the EMT for solving heterogeneous problems. A few preliminary studies employ domain adaptation techniques to enhance the transferability between two distinct tasks. However, almost all of these methods encounter a severe issue which is the so-called degradation of intertask mapping. Keeping this in mind, a novel rank loss function for acquiring a superior intertask mapping is proposed in this article. In particular, with an evolutionary-path-based representation model for optimization instance, an analytical solution of affine transformation for bridging the gap between two distinct problems is mathematically derived from the proposed rank loss function. It is worth mentioning that the proposed mapping-based transferability enhancement technique can be seamlessly embedded into an EMT paradigm. Finally, the efficacy of our proposed method against several state-of-the-art EMTs is verified experimentally on a number of synthetic multitasking and many-tasking benchmark problems, as well as a practical case study.

Affine Transformation-Enhanced Multifactorial Optimization for Heterogeneous Problems

Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach

Study of hydraulic fracturing in an anisotropic poroelastic medium via a hybrid EDFM-XFEM approach

\n Surrogate models, which have become a popular approach to oil-reservoir production-optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical simulator. In this paper, a new optimization algorithm called global and local surrogate-model-assisted differential evolution (GLSADE) is introduced for waterflooding production-optimization problems. The proposed method consists of two parts: (1) a global surrogate-model-assisted differential-evolution (DE) part, in which DE is used to generate multiple offspring, and (2) a local surrogate-model-assisted DE part, in which DE is used to search for the optimum of the surrogate. The cooperation between global optimization and local search helps the production-optimization process become more efficient and more effective. Compared with the conventional one-shot surrogate-based approach, the developed method iteratively selects data points to enhance the accuracy of the promising area of the surrogate model, which can substantially improve the optimization process. To the best of our knowledge, the proposed method uses a state-of-the-art surrogate framework for production-optimization problems. The approach is tested on two 100-dimensional benchmark functions, a three-channel model, and the egg model. The results show that the proposed method can achieve higher net present value (NPV) and better convergence speed in comparison with the traditional evolutionary algorithm and other surrogate-assisted optimization methods for production-optimization problems.

Global and Local Surrogate-Model-Assisted Differential Evolution for Waterflooding Production Optimization

To investigate the characteristics of oil distribution in porous media systems during a high water-cut stage, sandstones with different permeability scales of 53.63 ×10-3 μm 2 and 108.11 ×10-3 μm 2 were imaged under a resolution of 4.12 μm during a water flooding process using X-ray tomography. Based on the cluster-size distribution of oil segmented from the tomography images, and through classification using the shape factor and Euler number, the transformation of the oil distribution pattern in different injection stages was studied for samples with different pore structures. In general, the distribution patterns of an oil cluster continuously change during water injection. Large connected oil clusters break off into smaller segments. For sandstones with higher permeability, which show the largest change in distribution pattern, and the remaining oil is trapped in the pores with a radius of approximately 7-12 μm. Meanwhile, some disconnected clusters merge and lead to a re-connection during the high water cut period. Whereas the pore structure becomes compact and complex, the residual non-wetting phase becomes static and is difficult to move, and thus all distribution patterns coexist during the entire displacement process, and are mainly distributed in pores with a radius of 8-12 μm. For the pore-scale entrapment characteristics of the oil phase during a high water cut period, different enhance oil recovery (EOR) methods should be considered in sandstones correspondent to each permeability scale.

/pdf/microscopic-determination-of-remaining-oil-distribution-in-1izttg25v5.pdf

Microscopic Determination of Remaining Oil Distribution in Sandstones With Different Permeability Scales Using Computed Tomography Scanning

Hydraulic fracturing is an important measure for the development of tight reservoirs. In order to describe the distribution of hydraulic fractures, micro-seismic diagnostic was introduced into petroleum fields. Micro-seismic events may reveal important information about static characteristics of hydraulic fracturing. However, this method is limited to reflect the distribution area of the hydraulic fractures and fails to provide specific parameters. Therefore, micro-seismic technology is integrated with history matching to predict the hydraulic fracture parameters in this paper. Micro-seismic source location is used to describe the basic shape of hydraulic fractures. After that, secondary modeling is considered to calibrate the parameters information of hydraulic fractures by using DFM (discrete fracture model) and history matching method. In consideration of fractal feature of hydraulic fracture, fractal fracture network model is established to evaluate this method in numerical experiment. The results cle...

https://www.worldscientific.com/doi/pdf/10.1142/S0218348X18400091

Parameter prediction of hydraulic fracture for tight reservoir based on micro-seismic and history matching

The distribution of fractures is highly uncertain in naturally fractured reservoirs (NFRs) and may be predicted by using the assisted-history-matching (AHM) that calibrates the reservoir model acco...

https://www.worldscientific.com/doi/pdf/10.1142/S0218348X19400085

A fractal discrete fracture network model for history matching of naturally fractured reservoirs

It is necessary to optimize the operation of micro power grid in order to improve its economy. Firstly, the mathematical model of optimal dispatching of microgrid is established. Taking the internal battery charge and discharge power, fan and photovoltaic output power of microgrid as the optimization vector and the minimum operation cost of microgrid as the optimization objective, based on the day ahead prediction data, the day is divided into 24h as a dispatching period for optimization. The Twin Delayed Deep Deterministic policy gradient algorithm (TD3) is used to solve the problem, and the power supply composition and cost data are obtained. The simulation results verify the effectiveness of the algorithm and optimal scheduling strategy.

Optimal Operation Strategy of Microgrid Based on TD3 Algorithm

The fracturing technique is widely used in many fields. Fracture has a greater impact on the movement of fluids in formations. Knowing information about a fracture is key to judging its effect, but detailed information about complex fracture networks is difficult to obtain. In this paper, we propose a new method to describe the shape of a complex fracture network. This method is based on microseismic results and uses the L-system to establish a method for characterizing a complex fracture network. The method also combines with decomposition to construct a new method called the multiobjective fracture network inversion algorithm based on decomposition (MOFNIAD). The coverage of microseismic monitoring results and the degree of fitting of production data are the two objective functions of the inversion fracture network. The multiobjective fracture network inversion algorithm can be optimized to obtain multiple optimal solutions that meet different target weights. Therefore, this paper established a multischeme decision method that approached the ideal solution, sorting technology and AHP to provide theoretical guidance for finding a more ideal fracture network. According to the error of microseismic monitoring results, we established two cases of fracture to verify the proposed method. Judging from the results of the examples, the fracture network finally obtained was similar to actual fractures.

Monitoring the Geometry Morphology of Complex Hydraulic Fracture Network by Using a Multiobjective Inversion Algorithm Based on Decomposition

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Papers

Parameter prediction of hydraulic fracture for tight reservoir based on micro-seismic and history matching

A fractal discrete fracture network model for history matching of naturally fractured reservoirs

Optimal Operation Strategy of Microgrid Based on TD3 Algorithm

Monitoring the Geometry Morphology of Complex Hydraulic Fracture Network by Using a Multiobjective Inversion Algorithm Based on Decomposition