47 research outputs found

    An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification

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    Shear sonic wave velocity (Vs) has a wide variety of implications, from reservoir management and development to geomechanical and geophysical studies. In the current study, two approaches were adopted to predict shear sonic wave velocities (Vs) from several petrophysical well logs, including gamma ray (GR), density (RHOB), neutron (NPHI), and compressional sonic wave velocity (Vp). For this purpose, five intelligent models of random forest (RF), extra tree (ET), Gaussian process regression (GPR), and the integration of adaptive neuro fuzzy inference system (ANFIS) with differential evolution (DE) and imperialist competitive algorithm (ICA) optimizers were implemented. In the first approach, the target was estimated based only on Vp, and the second scenario predicted Vs from the integration of Vp, GR, RHOB, and NPHI inputs. In each scenario, 8061 data points belonging to an oilfield located in the southwest of Iran were investigated. The ET model showed a lower average absolute percent relative error (AAPRE) compared to other models for both approaches. Considering the first approach in which the Vp was the only input, the obtained AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.54%, 1.34%, 1.54%, 1.56%, and 1.57%, respectively. In the second scenario, the achieved AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.25%, 1.03%, 1.16%, 1.63%, and 1.49%, respectively. The Williams plot proved the validity of both one-input and four-inputs ET model. Regarding the ET model constructed based on only one variable,Williams plot interestingly showed that all 8061 data points are valid data. Also, the outcome of the Leverage approach for the ET model designed with four inputs highlighted that there are only 240 "out of leverage" data sets. In addition, only 169 data are suspected. Also, the sensitivity analysis results typified that the Vp has a higher effect on the target parameter (Vs) than other implemented inputs. Overall, the second scenario demonstrated more satisfactory Vs predictions due to the lower obtained errors of its developed models. Finally, the two ET models with the linear regression model, which is of high interest to the industry, were applied to diagnose candidate layers along the formation for hydraulic fracturing. While the linear regression model fails to accurately trace variations of rock properties, the intelligent models successfully detect brittle intervals consistent with field measurements

    Understanding the Flow Mechanism of Fuyu oil layer in a certain Block

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    The Fuyu oil layer in a certain block is an ultra-low permeability oil layer. The development scale of sand bodies in the Fuyu oil layer is narrow, with rapid changes in facies on the plane, strong reservoir heterogeneity, and complex oil and gas accumulation and oil water distribution. Understanding the flow mechanism of the Fuyu oil layer in a certain block can help improve the development effect of this bloc

    Optimization of the combination method for tertiary oil recovery layers in Block N

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    This article analyzes the development status of oil layers in Zone N, and based on the principles of layer combination in other injected blocks in Zone X, compares the development status of oil layers in Block M, and formulates two sets of layer combination plans for tertiary oil recovery in Block N. By comparing and optimizing the two sets of layer combination plans, the final decision is made to conform to the combination plan for tertiary oil recovery in Block N

    Products and Services

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    Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

    On the optimization of CO2-EOR process using surrogate reservoir model

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    CO2-EOR projects are becoming increasingly popular. Since enhanced recovery processes are applied to the mature fields, it usually involves a large number of wells. While the large number of wells leads to a better geological model, it results in very large flow models that are hard to manage, history match, and use as an optimization base. Nevertheless, injection-production optimization remains the core of all modeling efforts in CO2-EOR projects.;The objective of this work is to investigate the feasibility of using state-of-the-art data-driven proxy models to facilitate injection-production optimization in a CO2-EOR process. The use of coupled grid-based---SRM G and well-based---SRMW Surrogate Reservoir Model (as a proxy that runs in seconds) will be investigated as a tool to achieve the objectives of this study. The coupled SRM is built based on a reservoir simulation model that is developed for this purpose. The coupled SRM will be able to identify the dynamic reservoir properties (pressure, saturations, and component mole fraction at gridblock level) throughout the reservoir, along with the production characteristics at each well. It can be used to identify the optimum injection strategy (volume, rate, etc.) that would result in increased oil production.;The EOR technique that is attracting the most new market interest is CO2-EOR. First tried in 1972 in Scurry County, Texas, CO2 injection has been used successfully throughout the Permian Basin of West Texas and eastern New Mexico. The SACROC field, a depleted oil field located in western Scurry County in Texas, is the subject of this study.;A high resolution geological model was built for the northern platform. The model is based on a comprehensive geological study including 3D seismic survey and well logs. The porosity and permeability data for the fine grids were obtained from the Bureau of Economic Geology (BEG). The very long run-time of the reservoir simulation model that is the result of complexity of the reservoir makes it impractical to perform any sensitivity analysis, uncertainty analysis, or optimization study on the model. In order to overcome this problem, developing a surrogate reservoir model based on Artificial Intelligence and Data Mining techniques was planned. The coupled SRM provides the means for performing a large number of simulation runs, in short period of time, to be used for uncertainty quantification, and search of solution space for optimization.;Multiple injection scenarios were designed and run using a numerical reservoir simulator. The results were used in order to build a comprehensive spatio-temporal dataset, which includes all aspects of the reservoir model that is needed to train, calibrate, and validate the coupled SRM. From the parameters assimilated to form the comprehensive spatio-temporal dataset, Key Performance Indicators were identified and ranked. These KPIs helped to determine the dimensionality of the input space used to develop the SRMs (SRM W and SRMG).;Optimization may be identified by two focus areas. Building an efficient evaluation function and finding the quickest path to global minima. In this work, we focus on the efficiency of the evaluation function. The integrated SRM was built by coupling the two aforementioned SRMs. This SRM can be used to identify the optimal injection strategy (volume, rate, etc.) that would result in increased oil production while keeping an eye on the flood front

    Remote Sensing of the Oceans

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    This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

    Fundamentals of Enhanced Oil Recovery

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    For many years, the trend of increasing energy demand has been visible. Despite the search for alternative energy sources, it is estimated that oil and natural gas will be the main source of energy in transport for the next several dozen years. However, the reserves of renewable raw materials are limited in volume. Along with the degree of depletion, oil recovery becomes more and more difficult, even though the deposits are not yet completely empty. Therefore, it is essential to find new methods to increase oil and gas recovery. Actions aimed at intensifying oil recovery are very rational use of energy that has not yet been fully used. Usually, an increase in oil recovery can be achieved by using extraction intensification methods. However, measures to increase oil recovery can be implemented and carried out at any stage of the borehole implementation. Starting from the well design stage, through drilling and ending with the exploitation of oil and gas. Therefore, in order to further disseminate technologies and methods related to increasing oil recovery, a special edition has been developed, entitled "Fundamentals of Enhanced Oil Recovery". This Special Issue mainly covers original research and studies on the above-mentioned topics, including, but not limited to, improving the efficiency of oil recovery, improving the correct selection of drilling fluids, secondary methods of intensifying production and appropriate energy management in the oil industry
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