Accurate determination of effective lower limit and establishment of effective standard of tight reservoirs are of great significance for effective reserves estimation and production increase in tight oil. Based on convolution neural network and multivariate statistical analysis, establishes a model for lithology identification and petrophysical facies interpretation of tight reservoirs, and establishes a multi-parameter fusion method for evaluating the effectiveness of tight reservoirs by combining petrophysical facies classification modeling and pore structure evaluation on the basis of reservoir characteristics description. The actual data processing results show that the prediction accuracy of the established lithology identification method is high, and the comprehensive evaluation results are in agreement with the data of oil test and production test. This paper comprehensively considers the main factors that affect the effectiveness of tight reservoirs, and combines the lithology, physical properties and pore structure types of tight reservoirs to establish a comprehensive effectiveness evaluation method, which can be effectively applied to the comprehensive evaluation of tight reservoirs effectiveness.

The tight beach-bar sand reservoir in the study area has rich petroleum reserves and high exploration and development potential. However, it is characterized by deep burial, thin single-layer thickness, ultra-low permeability, complex pore structure, and extremely low natural productivity of the single well and it is difficult to classify the reservoir and identify the lithology. Based on the characteristic of time sequence logging data, a bi-directional long short-term memory neural network (BiLSTM) lithology identification model is constructed. The Random Forest method is used to conduct feature selection for conventional logging parameters and other parameters. The selected parameters are used as input variables to train the BiLSTM model. The model is applied to validate the well data of the test set, and the results showed that the accuracy of the model is 0.86, achieving good application results. This proves that BiLSTM model is suitable for the lithology identification of the beach-bar sand reservoir.

鄂尔多斯盆地东缘临兴-神府区块是典型的低渗透、低产量、低丰度致密岩性圈闭气藏,气藏勘探难度大,主力层位是煤系地层山西、太原、本溪组.以煤系地层致密砂岩储层地质和测井基础理论为指导,综合利用岩心测试、地质和测井等资料,结合电成像测井资料,围绕临兴-神府地区目标储层展开测井资料解释综合评价.研究区岩性共划分出砾岩、粗砂岩、中砂岩、细砂岩、粉砂岩、碳质泥岩、泥岩、碳酸盐岩及煤9种岩性,总结建立7种常见层理构造的成像图像、倾角矢量及地质模型一体化的综合识别模式.结合区域背景认为研究区本溪组、太原组为有障壁滨岸沉积环境,主要包括潮汐水道、泻湖沼泽、混合坪、碳酸盐岩丘等沉积微相;山西组为三角洲前缘沉积环境,主要包括水下分流河道、河道间、河口坝3种微相.该研究对于该地区天然气勘探开发有一定的指导意义.

深度学习是人工智能中的一个重要部分,卷积神经网络作为深度学习一个分支,用多层非线性计算单元可以表达高度非线性和高变度函数.提出将卷积神经网络应用于判别储层岩性的方法,构建了一个双层的卷积神经网络模型,样本回判准确率为99％.通过把卷积神经网络方法与岩石物理相方法和支持向量机方法进行对比,分析卷积神经网络方法准确率高、速度快,岩性预测具有实时性.由此证明卷积神经网络在储层岩性识别中的适用性,且准确率较高.

由于砾石成分复杂、孔隙结构多样、非均质性强,流体对测井响应的影响远小于岩石骨架,导致常规测井技术识别砂砾岩中的流体比较困难.为此,提出将机器学习AdaBoost.M2算法运用于砂砾岩流体识别中.应用该算法,结合试油、试采资料,将K类多流体类型拆解为K-1个二分类问题,通过多轮迭代得到样本分布,然后调用决策树算法作为弱学习算法自动得到分类器ht进行判别.将该方法应用于A研究区砂砾岩流体识别中,样本回判准确率为95％,测试准确率达91.5％,证明了该方法的适用性,为常规测井识别砂砾岩流体性质提供了新的方法,对油气开采具有一定的指导意义.

PreviousNext No AccessInternational Geophysical Conference, Qingdao, China, 17-20 April 2017Method of fluid identification in carbonate reservoir based on particle swarm optimization and fuzzy c-means clustering algorithm (PSO-FCM)Authors: Zhang Yan*Chen GanghuaZhang Yan*School of Geosciences, China University of Petroleum (East China), QingdaoSearch for more papers by this author and Chen GanghuaSchool of Geosciences, China University of Petroleum (East China), QingdaoSearch for more papers by this authorhttps://doi.org/10.1190/IGC2017-203 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract A method of fluid identification based on particle swarm optimization and fuzzy c-means clustering algorithm (PSOFCM) is proposed for carbonate reservoir. The weight of curves is determined on the basis of fluid indicator. After curves weighted and normalized, the objective function of fuzzy c-means clustering algorithm (FCM) is selected as fitness function of particle swarm optimization (PSO) to obtain global optimal solution, thus improving the disadvantage that clustering result is easily disturbed by initial value. Fluid clustering centers are determined using PSO-FCM algorithm and test data, then it's used to calculate fluid probability in reservoir. Fluid identification standard is established by threshold of fluid probability using cross plot method. This method is applied in reef gas reservoir in northeast of Sichuan area. The accuracy rate of fluid identification is 93.2% in sample reservoir, 90.6% in non-sample reservoir. It shows that this method is reliable with higher identification accuracy and provides beneficial guides for fluid identification in carbonate reservoir using conventional well logging curves. Keywords: fluid, borehole geophysics, algorithm, carbonatePermalink: https://doi.org/10.1190/IGC2017-203FiguresReferencesRelatedDetails International Geophysical Conference, Qingdao, China, 17-20 April 2017ISSN (online):2159-6832Copyright: 2017 Pages: 1525 publication data© 2017 Published in electronic format with permission by the Society of Exploration Geophysicists and Chinese Geophysical SocietyPublisher:Society of Exploration Geophysicists HistoryPublished Online: 31 May 2017 CITATION INFORMATION Zhang Yan* and Chen Ganghua, (2017), "Method of fluid identification in carbonate reservoir based on particle swarm optimization and fuzzy c-means clustering algorithm (PSO-FCM)," SEG Global Meeting Abstracts : 803-806. https://doi.org/10.1190/IGC2017-203 Plain-Language Summary Keywordsfluidborehole geophysicsalgorithmcarbonatePDF DownloadLoading ...

Aimed at the difficult problem of fluid identification in complex carbonate reservoirs,a method of establishing the fluid identification factor by joint acoustic and resistivity logging was proposed.After calculating elastic parameters by array acoustic logging data,etc.,fluid indicator coefficient was established to investigate fluid sensitivity of the elastic parameters,then the sensitive parameters were selected to build the new fluid identification factor.Based on Gassmann theory and Archie formula,it is proved that conventional fluid identification factor (F) and resistivity parameter (R0 /Rt) had consistency in indicating relative contents of gas and water in the gas-water two phases homogeneous medium.Hereby,a new fluid identification factor from joint use of acoustic logging and resistivity logging was built.Then,fluid indicator coefficients of several fluid identification factors were compared.The result indicated that the new fluid factor had a greater fluid indicator coefficient and stronger ability of fluid identification than the others.According to the cross plot of the new fluid identification factor and the ratio of P-wave velocity and S-wave velocity,the regional fluid identification standard was determined.The application results of the new identification factor to the carbonate reservoir in the Northeast Sichuan show that interpretation results are highly consistent with the conclusions of test data.The new fluid identification factor provides beneficial reference for fluid identification in carbonate reservoirs in the Northeast Sichuan.

AbstractThe protection of high porosity and high permeability reservoirs has not got enough attentions because the excellent hydrocarbon storage capacity would provide high oil and gas productivity even being damaged to some extent. Deepwater oil and gas reservoirs are mainly high porosity and permeability reservoirs, and the reservoir protection is important because severe reservoir damage will cause an enormous economic loss due to high investment and risk of deepwater operation. A new deepwater gas field in the western part of the South China Sea must be developed soon, and the reservoir protection strategy needs to be studied.The characteristics of the reservoir were studied by X-ray diffraction, scanning electron microscope, mercury intrusion porosimetry, hot rolling dispersion test and liner swelling test. Furthermore, the water and salinity sensitivities of the reservoir were studied. The results show that the reservoir has high porosity and high permeability with an average porosity larger than 30% and an average permeability higher than 500×10-3 μm. The large pore throats will be easily invaded and plugged by solids in drilling and completion fluid. Therefore, effective temporary-plugging of the pore throats with a wide size range is extremely important. The content of clay mineral is 9% to 19% with approximately 50% mixed-layer illite-smectite, presenting potential borehole instability and reservoir damage risk due to its strong hydration ability. In addition, the reservoir has strong salinity sensitivity and medium-weak water sensitivity. The reservoir protection strategy of drilling and completion fluid related to the properties of the reservoir was proposed. A multi-stage bridge temporary-plugging method was used to prevent the invasion of solids into the pore throats with a wide size range. A highly inhibitive polyamine was selected to inhibit the hydration of clays to maintain wellbore stability and reduce reservoir damage. Because of the requirement of gas hydrate inhibition and density adjustment, a high performance water-based drill-in fluid with formate brines was developed. It has excellent reservoir protection performance with the permeability recovery rate approximately 90%. Meanwhile, it performs well in inhibiting clay hydration, hydrate formation, and has good rheological as well as filtration properties over a wide temperature range from 4 °C to 75 °C.This work provides theoretical guidance of reservoir protection and a high performance drill-in fluid for the target deepwater gas filed. We hope it will also be useful for the protection of high porosity and high permeability reservoir in other deepwater oil and gas fields.

针对砂砾岩地层岩性变化大、非均质性强、常规测井曲线影响因素多、砂砾岩地层地质特征与测井曲线呈现非线性关系等特点,采用支持向量机方法(SVM)对地层岩性进行划分.选用粒子群算法对支持向量机参数进行优化,得到岩性识别模型;根据模型对研究区的30多口井的岩性进行划分,取得良好的地质应用效果.

In order to give an optimal design of methane hydrate inhibitors for deep-sea drilling, the most popular thermodynamic hydrate inhibitors (THIs) including NaCl and glycol, kinetic hydrate inhibitors (KHIs) including poly (vinyl pyrrolidone) and poly (vinylcaprolactam), and the mixtures of these inhibitors were experimentally tested in drilling fluid under the seabed conditions at different water depths. Moreover, a high performance KHI was developed and was proved to be a better one than the typical KHIs. Based on the performance tests and compatibility tests, the optimal hydrate inhibitors were designed for drilling deep-sea wells located at different water depth.

The overlap and denudation zone of southern Dongying goes short of sedimentary interruption,stratigraphic dip and imaging data.The seismic data is difficult to accurately determine the unconformity interface.In the macroeconomic regulation and control of seismic data,based on conventional log data,we divided the unconformity interfaces in the area into two levels(Ⅰ level and Ⅱ level)by the ordered sequence segmentation method.We establish comprehensive layered log curves by picking up corresponding sensitive parameters,which can accurately determine the location of unconformity interface.The results show that unconformity interface depth determined by logging data is consistent with geological results.This method provides an effective and feasible way to divide the denudation landforms.

Abstract In order to accelerate the drilling speed and reduce the drilling cost, the well structures were simplified in Tahe oil field in recent years. As a result, the length of open hole section increased dramatically, even as high as 4700m for some wells, and different formations including active shales, gypsum, and limestone were encountered in the drilling process. Drilling in such long interval of open hole undertook the risks of cavings, stuck pipe, hole enlargement, lost circulation and so on because of the complex formation and changing pressure gradients. Therefore, highly inhibitive water-based drilling fluids were essential to improve the wellbore stability of the challenging sections. To understand the mechanisms of shale-drilling fluid interactions when exposed to water-based drilling fluids, the morphology and mineralogy of shale samples were characterized via scanning electron microscope (SEM) and X-ray analysis. Meanwhile, inhibitive evaluation methods such as swelling test, dispersion test and cation exchange capacity (CEC) test were carried out to analyze the interaction potential of shale samples from the open hole intervals. The results indicated that for the upper interbedded sandstone and shale formation, impressive content of clay minerals (mainly smectite) and weak bonding were contributed to the high potential of swelling and dispersion. While for the deep brittle shale formations, the content of clay mineral (mainly illite and illite/smectite) was also relatively high, and micro-fractures were observed, which provided the access of water invasion. After analyzing the wellbore instability mechanism of the open hole interval, different drilling fluid strategies were proposed according to the reactivity of formations. For shallow formation, polyamine polymer water-based drilling fluid was selected to suppress the hydration and dispersion of reactive shales. While for the deep formation, polyamine and sulfonated polymer water-based drilling fluid was optimized, in which polyamine as shale hydration inhibitor, sulfonated asphalt and superfine calcium carbonate as microfracture sealing agent were used in combination to realize the chemical and physical stability of brittle shales. The optimized inhibitive water-based drilling fluids in laboratory were successfully transferred to the application in the long open hole intervals of Tahe oil field. The field application in this area resulted in reduced cost, improved gauge hole and reduction in total NPT. No complicated problems occurred in the field trial. The application indicated that elaborately designed drilling fluids reached satisfying results.

缝洞是碳酸盐岩储层重要的储集空间和渗流通道,缝洞充填物的识别对评价油气储集能力和渗流能力具有重要作用.解决缝洞充填物与常规测井响应之间的非线性问题,BP神经网络法具有突出的优势.为此,通过结合成像测井和岩心资料,将碳酸盐岩储层缝洞充填物划分为泥质充填、砂质充填和结晶碳酸盐岩充填3类类型;分析不同充填类型的缝洞测井响应特征,选取敏感性较强的泥质含量、裂缝孔隙度、中子比、密度比和深侧向电阻率等5个参数,利用BP神经网络建立了碳酸盐岩储层缝洞充填物的识别方法.应用所建立的方法对实际井资料进行了处理评价,其预测结果与实际结果有较好的一致性,取得了较好的应用效果.

Coalbed methane reservoir log data interpretation results often show multi-solutions,ambiguity and uncertainty due to its heterogeneity and anisotropy.Put forward is a method to improve network training accuracy and coalbed methane reservoir evaluation accuracy by combining genetic algorithm and neural network.This method uses genetic algorithm to optimize neural network connection weights and threshold.It increases computing speed by avoiding its disadvantages that standard BP algorithm is apt to trap in local minimal solution,and genetic algorithm is weak at the locally searching capability.Introduced is the process for optimizing neural network connection weights and threshold and coal quality parameters forecast.Established is a coal quality log evaluation model based on GA-BP neural network,learning-samples selection and network structure determination,and data normalizing.Comparative analysis of 26 samples shows that this algorithm has higher accuracy and faster processing speed.Practical applications in more than 10 wells indicate that the prediction results of GA-BP method match well with coal core test data,and have good consistency with volume model calculation results.

Based of X-ray analysis, conventional logging data combined with multielement nonlinear regression and grey theory analysis are applied to determine the clay mineral content in the area where is poor in natural gamma ray spectrometry log. The result shows that the method is accurate for determining the clay mineral content, so it can provide theory foundation for reservoir evaluation and sensitivity analysis.

Lithophase is the response of lithology and porosity,and it has an important controlling effect on the distribution of water and oil.Every lithophase have similar logging response,lithology characteristic and relationship of porosity and permeability.In this paper,the logging response of MRIL and FMI have been analyzed by using electrofacies analysis technique.The whole reservoir is divided into several lithophases according to logging response,well logging evaluation models have been constructed,and criterion to identify oil-water layers for different lithophase have been determinated.Through this study,the accuracy of oil-water layer identification has been improved from 60% to 80%,and the difficult problem to identify oil-water layer has been solved.