An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network

 The CO2 enhanced oil recovery (EOR) method is widely used in actual oilfields. It is extremely important to accurately predict the CO2 minimum miscibility pressure (MMP) for CO2-EOR. At present, many studies about MMP prediction are based on empirical, experimental, or numerical simulation methods, but these methods have limitations in accuracy or computation efficiency. Therefore, more work needs to be done. In this work, with the results of the slim-tube experiment and the data expansion of the multiple mixing cell methods, an improved artificial neural network (ANN) model that predicts CO2 MMP by the full composition of the crude oil and temperature is trained. To stabilize the neural network training process, L2 regularization and Dropout are used to address the issue of over-fitting in neural networks. Predicting results show that the ANN model with Dropout possesses higher prediction accuracy and stronger generalization ability. Then, based on the validation sample evaluation, the mean absolute percentage error and R-square of the ANN model are 6.99 and 0.948, respectively. Finally, the improved ANN model is tested by six samples obtained from slim-tube experiment results. The results indicate that the improved ANN model has extremely low time cost and high accuracy to predict CO2 MMP, which is of great significance for CO2-EOR.Cited as: Dong, P., Liao, X., Chen, Z., Chu, H. An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network. Advances in Geo-Energy Research, 2019, 3(4): 355-364, doi: 10.26804/ager.2019.04.0

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A well-testing method for parameter evaluation of multiple fractured horizontal wells with non-uniform fractures in shale oil reservoirs

 Owing to intricate geological and engineering factors, the hydraulic fractures in shale oil reservoirs sometimes are in heterogeneous and random lengths, which brings a difficulty in fracture estimation. To improve this situation, a simple and quick well-testing method is presented for fracturing evaluation and parameter estimation of multiple fractured horizontal wells with non-uniform fractures. The semianalytical method and Laplace transformation are used for model solution. With the proposed model, we estimate the properties of non-uniform fractures in shale oil wells from the Ordos Basin based on the buildup testing data. Results from the case studies show that there is a good relationship between fracturing treatment parameters and generated fracture properties, including fracture length and storativity ratio (or fracture volume ratio). The fracture parameter values increase with the increase in fracturing liquid volume, especially the inner region permeability and storativity ratio. When the fracturing liquid volume per stage increases by 200-300 m3 , the fracture impacts are weaker on generated parameters, which indicates that there would be an optimized fracturing liquid volume in the field case.Cited as: Meng, M., Chen, Z., Liao, X., Wang, J., Shi, L. A well-testing method for parameter evaluation of multiple fractured horizontal wells with non-uniform fractures in shale oil reservoirs. Advances in Geo-Energy Research, 2020, 4(2): 187-198, doi: 10.26804/ager.2020.02.0

Methodology for estimation of CO2 storage capacity in reservoirs

The CO2 storage in reservoirs is one of the most effective ways of reducing the greenhouse gas emission, which is based on the mechanisms of structural and stratigraphic trapping, residual gas trapping, dissolution trapping and mineral trapping. The CO2 storage capacity in oil reservoirs includes theoretical, effective, practical and matched storage capacities. In the estimation of the CO2 storage capacity in both waterflooding and CO2 flooding oil reservoirs, theoretical and effective storage capacities can be obtained by the material balance and analogy methods. The theoretical storage capacity represents the physical limit of what the reservoir system can accept. The effective storage capacity represents a subset of the theoretical capacity and is obtained by applying a range of technical cut-off limits to a storage capacity assessment which incorporate the cumulative effects of reservoir and fluid parameter. When the material balance method is used, the amount of CO2 dissolution is not negligible. In using the analogy method, the key is to determine CO2 utilization factor. Examples show that the method is simple and convenient for the estimation of the CO2 storage capacity in China. Key words: carbon dioxide, storage, waterflooding, carbon dioxide flooding, dissolution, algorith

A semianalytical well-testing model of fracture-network horizontal wells in unconventional reservoirs with multiple discretely natural fractures

Microseismic data shows that some unconventional reservoirs comprise well-developed natural fractures and complex hydraulic fracture networks. It is neither practical nor advantageous to simulate a huge number of natural and hydraulic fractures with numerical models. Given that the conventional dual-porosity models are not applicable to the highly discrete natural fractures, the paper develops a semianalytical well testing model for horizontal wells with hydraulic fracture networks and randomly-distributed discretely natural fractures.The proposed model has the capability to analyze the pressure behaviors by considering complex fracture networks and isolated natural fractures rapidly and efficiently. The model includes diffusivity equations in three domains: (1) matrix, (2) discretely natural fractures, and (3) hydraulic fracture networks. The pressure transient solution of these diffusivity equations is obtained by using Laplace transforms and superposition principle. We verify the presented model by performing a case study with a numerical simulator for complex natural fractures.It is found that there are some interesting flow behaviors for fracture-network horizontal well with discretely natural fractures like bilinear flow, “V-shape” caused by fluid supply, pseudo boundary-dominated flow, impact of natural fractures, etc. The pseudo boundary-dominated flow provides us the information about how large the area covered by hydraulic fracture networks. The impact of natural fracture shows the parameters of natural fractures. This work provides a good understanding of transient pressure behaviors in unconventional reservoirs and guidelines for the producer optimize field development and well economics

Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach

With the emergence of various online trading technologies, fraudulent cases begin to occur frequently. The problem of fraud in public trading companies is a hot topic in financial field. This paper proposes a fraud detection model for public trading companies using datasets collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs). At the same time, this computational finance model is solved with a nonlinear activated Beetle Antennae Search (NABAS) algorithm, which is a variant of the meta-heuristic optimization algorithm named Beetle Antennae Search (BAS) algorithm. Firstly, the fraud detection model is transformed into an optimization problem of minimizing loss function and using the NABAS algorithm to find the optimal solution. NABAS has only one search particle and explores the space under a given gradient estimation until it is less than an “Activated Threshold” and the algorithm is efficient in computation. Then, the random under-sampling with AdaBoost (RUSBoost) algorithm is employed to comprehensively evaluate the performance of NABAS. In addition, to reflect the superiority of NABAS in the fraud detection problem, it is compared with some popular methods in recent years, such as the logistic regression model and Support Vector Machine with Financial Kernel (SVM-FK) algorithm. Finally, the experimental results show that the NABAS algorithm has higher accuracy and efficiency than other methods in the fraud detection of public datasets

Advances on intelligent algorithms for scientific computing:An overview

The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas.</p

A Method for Evaluating the Dominant Seepage Channel of Water Flooding in Layered Sandstone Reservoir

A method for evaluating the dominant seepage channel (DSC) water flooding in a layered sandstone reservoir is proposed and applied in an oilfield based on the water-cut derivative. The water-cut derivative curve of the reservoir with DSC shows double peaks. Therefore, based on the analysis of geology and production characteristics, the evaluation method of DSC is established. The evaluation index is proposed to quantitatively characterize the development degree of DSC and determine its distribution in a water-flooding reservoir. The test data validate that the proposed method can not only accurately determine the DSC and quantitatively evaluate its development degree, but also show its dynamic change. This method will be a powerful guide for water controlling and oil stabilizing in the adjustment stage of sandstone reservoirs

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios