The construction of shale rock physics model and brittleness prediction for high-porosity shale gas-bearing reservoir

Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale rock physics model is significant for the exploration and development of shale reservoirs.To make a better characterization of shale gas-bearing reservoirs,we first propose a new but more suitable rock physics model to characterize the reservoirs.We then use a well A to demonstrate the feasibility and reliability of the proposed rock physics model of shale gas-bearing reservoirs.Moreover,we propose a new brittleness indicator for the high-porosity and organic-rich shale gas-bearing reservoirs.Based on the parameter analysis using the constructed rock physics model,we finally compare the new brittleness indicator with the commonly used Young’s modulus in the content of quartz and organic matter,the matrix porosity,and the types of filled fluids.We also propose a new shale brittleness index by integrating the proposed new brittleness indicator and the Poisson’s ratio.Tests on real data sets demonstrate that the new brittleness indicator and index are more sensitive than the commonly used Young’s modulus and brittleness index for the high-porosity and high-brittleness shale gas-bearing reservoirs.

Pore Connectivity of Deep Lacustrine Shale and its Effect on Gas-bearing Characteristics in the Songliao Basin:Implications from Continental Scientific Drilling

The lacustrine shale of deep Shahezi Formation in the Songliao basin has great gas potential,but its pore evolution,heterogeneity,and connectivity characteristics remain unclear.In this work,total organic carbon analysis,rock pyrolysis,X-ray diffraction field emission scanning electron microscopy,the particle and crack analysis system software,low-temperature nitrogen adsorption experiment,fractal theory,high-pressure mercury injection experiment and nuclear magnetic resonance experiment were used to study the Shahezi shale from Well SK-2.The result indicated that the organic pores in Shahezi shale are not developed,and the intergranular and intragranular pores are mainly formed by illitedominated clay.As the burial depth increases,the pore size and slit-shaped pores formed by clay decrease,and dissolved pores in the feldspar and carbonate minerals and dissolved fractures in the quartz increase.The pore evolution is affected by clay,compaction,and high-temperature corrosion.Based on the pore structure characteristics reflected by the pore size distribution and pore structure parameters obtained by multiple experimental methods,the pore development and evolution are divided into three stages.During stageⅠandⅡ,the pore heterogeneity of the shale reservoirs increases with the depth,the physical properties and pore connectivity deteriorate,but the gas-bearing property is good.In stageⅢ,the pore heterogeneity is the highest,its gas generation and storage capacity are low,but the increase of micro-fractures makes pore connectivity and gas-bearing better.

A Productivity Prediction Method Based on Artificial Neural Networks and Particle Swarm Optimization for Shale-Gas Horizontal Wells

In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas hor-izontal wells after fracturing,a new sophisticated approach is proposed in this study.This new model stems from the combination several techniques,namely,artificial neural network(ANN),particle swarm optimization(PSO),Imperialist Competitive Algorithms(ICA),and Ant Clony Optimization(ACO).These are properly implemented by using the geological and engineering parameters collected from 317 wells.The results show that the optimum PSO-ANN model has a high accuracy,obtaining a R2 of 0.847 on the testing.The partial dependence plots(PDP)indicate that liquid consumption intensity and the proportion of quartz sand are the two most sensitive factors affecting the model’s performance.

Intelligent prediction and integral analysis of shale oil and gas sweet spots

Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable when attempting to delineate unconventional features of shale oil and gas. Based on some mathematical algorithms such as fuzzy mathematics, machine learning and multiple regression analysis, an effective workflow is proposed to allow intelligent prediction of sweet spots and comprehensive quantitative characterization of shale oil and gas reservoirs. This workflow can effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data. Following the maximum subordination and attribute optimization principle, we establish a machine learning model by adopting the support vector machine method to arrive at multi-attribute prediction of reservoir sweet spot location. Additionally, multiple regression analysis technology is applied to quantitatively predict a number of sweet spot attributes. The practical application of these methods to areas of interest shows high accuracy of sweet spot prediction, indicating that it is a good approach for describing the distribution of high-quality regions within shale reservoirs. Based on these sweet spot attributes, quantitative characterization of unconventional reservoirs can provide a reliable evaluation of shale reservoir potential.

The influence factors of gas-bearing and geological characteristics of Niutitang Formation shale in the southern margin of Xuefeng Mountain ancient uplift: A case of Well Huangdi 1

In order to evaluate the geological characteristics and gas-bearing factors of Niutitang Formation within the Lower Cambrian of northern Guizhou,the Huangping area located at the southern edge of the ancient uplift belt of Xuefeng Mountain was selected as the target area,and Well Huangdi 1 was drilled for the geological survey of shale gas.Through geological background analysis and well logging and laboratory analysis such as organic geochemical test,gas content analysis,isothermal adsorption,and specific surface area experiments on Well Huangdi 1,the results show that the Niutitang Formation is a deep-water shelf,trough-like folds and thrust fault.The thickness of black shale is 119.95 m,of which carbonaceous shale is 89.6 m.The average value of organic carbon content is 3.55%,kerogen vitrinite reflectance value is 2.37% and kerogen type is sapropel-type.The brittle mineral content is 51%(quartz 38%),clay mineral content is 38.3%.The value of porosity and permeability are 0.5%and 0.0014 mD,which the reservoir of the Niutitang Formation belongs to low permeability with characteristics of ultra-low porosity.The gas content is 0.09‒1.31 m^3/t with a high-value area and a second high-value area.By comparing with the geological parameters of adjacent wells in the adjacent area,the accumulation model of“sediment control zone,Ro control zone,structure controlling reservoir”in the study area is proposed.Therefore,deep-water shelf-slope facies,Ro is between high maturity-early stage of overmaturity and well-preserved zones in the Niutitang Formation in this area are favorable direction for the next step of shale gas exploration.

Quantitative prediction of shale gas sweet spots based on seismic data in Lower Silurian Longmaxi Formation,Weiyuan area,Sichuan Basin,SW China

Sweet spots in the shale reservoirs of the Lower Silurian Longmaxi Formation in Weiyuan 201 Block of Sichuan Basin were predicted quantitatively using seismic data and fuzzy optimization method. First, based on seismic and rock physics analysis, the rock physics characteristics of the reservoirs were determined, and elastic parameters sensitive to shale reservoirs with high gas content were selected. Second, data volumes with high precision of the elastic parameters were obtained from pre-stack simultaneous inversion. The horizontal distribution of key parameters for shale gas evaluation were calculated based on the results of rock physics analysis. Then, the fuzzy evaluation equation was established by fuzzy optimization method with test and logging data of horizontal wells with similar operation conditions. key parameters affecting the productivity of horizontal wells were sorted out and the weights of them in the sweet spots quantitative prediction were worked out by fuzzy optimization to set up a sweet spots evaluation system. Three classes of shale gas reservoirs which including two kinds of sweet spots were predicted with the above procedure, and the sweet spots have been predicted quantitatively by combining the above prediction results with the testing production. The testing results of 7 verification wells proved the reliability of the prediction results.

Prediction of Shale Gas Pressure based on Multi-channel Seismic Inversion in Fuling

The accurate prediction of formation pressure is important in oil/gas exploration and development.However,the achievement of this goal remains challenging,due to insufficient logging data and the low predictive data accuracy from seismic data.In this work,a case study was carried out in the Baima area of Wulong,in order to develop a workflow for accurately predicting shale gas formation pressure.The multi-channel stack method was first used,as well as the inversion of single-channel seismic data,to construct velocity and density models of the formation.Combined with the existing welllogging data,the velocity and density models of the whole well section were established.The shale gas formation pressure was then estimated using the Eaton method.The results show that the multi-channel seismic stacking method has a higher accuracy than the inversion of the formation velocity obtained by the single-channel seismic method.The discrepancies between our predicted formation pressure and the actual formation pressure measurement are within an acceptable range,indicating that our workflow is effective.

Method and practice of deep favorable shale reservoirs prediction based on machine learning

A set of methods for predicting the favorable reservoir of deep shale gas based on machine learning is proposed through research of parameter correlation feature analysis principle, intelligent prediction method based on convolution neural network(CNN), and integrated fusion characterization method based on kernel principal component analysis(KPCA) nonlinear dimension reduction principle.(1) High-dimensional correlation characteristics of core and logging data are analyzed based on the Pearson correlation coefficient.(2) The nonlinear dimension reduction method of KPCA is used to characterize complex high-dimensional data to efficiently and accurately understand the core and logging response laws to favorable reservoirs.(3) CNN and logging data are used to train and verify the model similar to the underground reservoir.(4) CNN and seismic data are used to intelligently predict favorable reservoir parameters such as organic carbon content, gas content, brittleness and in-situ stress to effectively solve the problem of nonlinear and complex feature extraction in reservoir prediction.(5) KPCA is used to eliminate complex redundant information, mine big data characteristics of favorable reservoirs, and integrate and characterize various parameters to comprehensively evaluate reservoirs. This method has been used to predict the spatial distribution of favorable shale reservoirs in the Ordovician Wufeng Formation to the Silurian Longmaxi Formation of the Weirong shale gas field in the Sichuan Basin, SW China. The predicted results are highly consistent with the actual core, logging and productivity data, proving that this method can provide effective support for the exploration and development of deep shale gas.

Reservoir characteristics and genetic mechanisms of gas-bearing shales with different laminae and laminae combinations: A case study of Member 1 of the Lower Silurian Longmaxi shale in Sichuan Basin, SW China

Based on thin-section,argon-ion polished large-area imaging and nano-CT scanning data,the reservoir characteristics and genetic mechanisms of the Lower Silurian Longmaxi shale layers with different laminae and laminae combinations in the Sichuan Basin were examined.It is found that the shale has two kinds of laminae,clayey lamina and silty lamina,which are different in single lamina thickness,composition,pore type and structure,plane porosity and pore size distribution.The clayey laminae are about 100μm thick each,over 15%in organic matter content,over 70%in quartz content,and higher in organic pore ratio and plane porosity.They have abundant bedding fractures and organic matter and organic pores connecting with each other to form a network.In contrast,the silty laminae are about 50μm thick each,5%to 15%in organic matter content,over 50%in carbonate content,higher in inorganic pore ratio,undeveloped in bedding fracture,and have organic matter and organic pores disconnected from each other.The formation of mud lamina and silt lamina may be related to the flourish of silicon-rich organisms.The mud lamina is formed during the intermittent period,and silt lamina is formed during the bloom period of silicon-rich organisms.The mud laminae and silt laminae can combine into three types of assemblages:strip-shaped silt,gradating sand-mud and sand-mud thin interlayers.The strip-shaped silt assemblage has the highest porosity and horizontal/vertical permeability ratio,followed by the gradating sand-mud assemblage and sand-mud thin interlayer assemblage.The difference in the content ratio of the mud laminae to silt laminae results in the difference in the horizontal/vertical permeability ratio.

Structure analysis of shale and prediction of shear wave velocity based on petrophysical model and neural network

Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.

Predicting the present-day in situ stress distribution within the Yanchang Formation Chang 7 shale oil reservoir of Ordos Basin, central China

The Yanchang Formation Chang 7 oil-bearing layer of the Ordos Basin is important in China for producing shale oil.The present-day in situ stress state is of practical implications for the exploration and development of shale oil;however,few studies are focused on stress distributions within the Chang 7 reservoir.In this study,the present-day in situ stress distribution within the Chang 7 reservoir was predicted using the combined spring model based on well logs and measured stress data.The results indicate that stress magnitudes increase with burial depth within the Chang 7 reservoir.Overall,the horizontal maximum principal stress(SHmax),horizontal minimum principal stress(Shmin) and vertical stress(Sv) follow the relationship of Sv≥SHmax>Shmin,indicating a dominant normal faulting stress regime within the Chang 7 reservoir of Ordos Basin.Laterally,high stress values are mainly distributed in the northwestern parts of the studied region,while low stress values are found in the southeastern parts.Factors influencing stress distributions are also analyzed.Stress magnitudes within the Chang 7 reservoir show a positive linear relationship with burial depth.A larger value of Young's modulus results in higher stress magnitudes,and the differential horizontal stress becomes higher when the rock Young's modulus grows larger.

Shale gas production evaluation framework based on data-driven models

Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.

基于时间序列相似性与机器学习方法的页岩气井产量预测

页岩气井单变量产量预测存在较强的不确定性,而现场生产动态数据同时包括多个相关指标,针对如何选取合理的多变量数据对页岩气井产量进行预测,在保证计算效率的情况下提高预测精度。页岩气井的生产动态数据集包括日产气量、日产水量、套压、油压、油嘴直径、开井时间和温度等,采用欧式距离和动态时间弯曲距离对生产动态数据时间序列进行相似性度量,依据与日产气量的相关度,把数据分为强相关时间序列和弱相关时间序列;其次,基于卷积神经网络、循环神经网络、长短期记忆网络和门控神经网络分别对全时间序列、强相关序列、弱相关序列和单变量序列进行页岩气井产量预测;最后,以平均绝对误差、均方根误差和决定系数作为评价指标,得到不同序列的误差由小到大排序为强相关序列、全时间序列、弱相关序列、单变量序列,优选的机器学习方法为门控神经网络和长短期记忆网络。结果表明,采用机器学习方法结合页岩气井强相关性序列(日产气量、套压、油压、日产水量)能有效降低预测误差,提高页岩气井产量预测效果。

页岩油注CO_(2)过程中重有机质沉积预测

综合考虑页岩基质纳米孔隙中吸附引发的限域效应、毛管压力作用及极性分子间的缔合作用,建立页岩油重有机质沉积预测模型及求解方法,并选取典型区块页岩油样开展重有机质沉积预测,对比分析页岩油重有机质沉积相边界的变化规律及CO_(2)注入对重有机质沉积相边界的影响。结果表明:缔合作用加剧低温区间的重有机质沉积风险,而高压区间内的重有机质沉积现象受纳米限域效应的影响更加显著;考虑耦合效应时,典型油样在CO_(2)注入量(摩尔分数)仅为5%时的重有机质沉积压力上限可达26.57 MPa,相比于未注气的情况下提高3 MPa;页岩油注CO_(2)过程中的重有机质沉积风险最大,控制CO_(2)注入量和避免地层压力降低过快是潜在的规避重有机质沉积风险的有效手段。

渝东南复杂构造区常压页岩气地球物理勘探实践及攻关方向

中国南方常压页岩气资源潜力大,主要分布于四川盆地周缘构造复杂区及盆外褶皱带,具有地表、地下双复杂地质条件,地震采集品质差、成像精度低、甜点参数变化规律不清。本文系统总结了渝东南地区常压页岩气地震采集、成像处理、储层预测等方面的研究成果与技术进展:①形成了变密度三维观测系统设计技术、灰岩地表复杂山地地震激发接收技术,确保复杂地下构造反射波场充分采样,提升采集资料品质,提高施工效率。②完善了复杂山地地震叠前预处理技术、盆缘过渡带复杂构造成像技术、盆外褶皱带向斜构造成像技术,成果剖面信噪比高,有效频带宽,构造成像精度高。③基于岩石物理特征研究,实现优质页岩厚度、地层压力系数、脆性的定量预测;基于统计岩石物理,实现页岩有机碳含量、含气量、孔隙度的定量预测;利用有限元应力场模拟技术,揭示古应力场演化,实现多期构造改造叠加作用形成裂缝的定量预测;采用组合弹簧模型今应力场预测技术,明确今地应力场分布规律。通过攻关研究,有效指导了常压页岩气甜点预测与勘探开发,为南川常压页岩气田的发现提供了依据。下步应重点攻关基于5G无线节点接收的更加科学合理的地震采集技术、复杂山地高陡构造高精度自动化成像处理技术,以及“地质—工程—经济”一体化的甜点地震评价方法。

天然气生产企业碳排放核算与预测方法研究--以四川盆地天然气开发为例

碳中和已成全球共识,世界能源体系正加速向低碳化演变。中国能源发展进入新时代,“双碳”目标成为经济社会发展根本遵循,作为碳排放主要来源的能源行业正大力实施绿色低碳发展战略,但要实现“双碳”目标,需建立统一规范的碳排放统计核算体系,并对碳排放量进行科学预测。选取天然气生产企业的碳排放核算及预测方法作为研究对象,基于天然气生产企业作业链,系统性梳理出天然气勘探、开发、处理、输送各环节的碳排放特征,并归类为化石燃料燃烧排放、火炬系统燃烧排放、工艺放空排放、电力消耗隐含排放等四大碳排放源,建立起碳排放核算与预测方法。通过选取四川盆地典型的常规气、页岩气、高含硫气藏进行实例测算与分析,研究结果表明:①鉴于不同类型气藏生产的碳排放结构和强度存在较大差异,为保证核算和预测结果的可靠性,对于涉及到多种类型气藏开发的企业,在碳排放核算和预测时要分类进行,并加强基础数据的统计与监测;②在能耗双控向碳排放双控转变的大趋势下,为促进油气行业的高质量发展,在建立碳排放双控指标时,应以碳排放强度指标为核心,以碳排放总量指标为辅,针对不同类型的气藏、建立不同的碳排放强度动态标准;③天然气生产企业既是甲烷的主要生产供应者、也是甲烷排放者,应加强对甲烷减排控排措施的研究。结论认为,该研究成果可以为天然气行业建立更为科学合理的碳排放核算与预测方法体系提供参考和借鉴。

页岩弹性模量的预测方法研究

页岩的弹性模量对于岩石工程具有十分重要的意义,但由于页岩是典型的横观各向同性岩石,这为预测其弹性模量带来了较大难度。常用的页岩弹性模量的预测方法为Niandou模型,但是该模型方程的形式较复杂,参数确定较为繁琐。本文针对页岩弹性模量的各项异性,提出了一种较为简易的线性经验模型。结果表明:本文提出的经验模型比经典的Niandou模型的方程形式较简易,所需的弹性参数较少,能够随着层理夹角的变化预测弹性模量;经验模型较Niandou模型整体平均误差略小,预测效果较好;尤其当层理面与加载方向的夹角为15°、30°和75°时,经验模型的预测相对误差更小,准确率较Niandou模型更高。

复杂油气藏储层预测方法综述

随着油气勘探不断深入,传统储层逐渐减少,使得岩溶储层、裂缝储层、页岩储层、煤层气等复杂储层成为重要勘探目标。这些复杂储层往往具有多种非均质性特征,包括岩性变化、孔隙裂缝、多介质分布等,导致储层的空间分布和特性变化极为复杂,深入研究复杂储层预测方法具有重要的理论和应用价值。本文重点分析了岩溶储层、裂缝储层、页岩储层和煤层气储层等复杂油气藏的形成机理和勘探难点;系统总结了相关研究的发展情况和应用方法,为相关领域研究人员提供了有益的参考。指出未来复杂储层预测的发展将集中在多源信息融合、数据集成、高分辨率地震技术和人工智能等方向,这些技术的应用有望提高预测精度和效率,为复杂气藏的勘探和开发提供更为全面和有效的支持。

基于树形回归法预测页岩油藏压裂水平井产能研究

页岩油井水平段体积压裂段数多且产能差异大,常规产能预测和压裂效果评价难度大,借助机器学习建立一种稳定、高效的智能产能预测方法是提升页岩油藏开发的有效途径。使用吉木萨尔页岩油藏的91口生产井的地质参数、工程参数和生产数据库,基于热力学图版和特征参数相关性分析对参数进行验证,从14个特征参数中确定6个涵盖地质因素与施工因素的最佳主控因素。采用树形回归法的决策树(DT)、随机森林(RF)和梯度提升决策树(GBDT)三种机器学习方法进行产量预测建模,利用均方根误差对模型性能进行评估。研究结果表明,含水率、含油饱和度、加砂量、压裂液用量、压裂段簇数和压裂级数是影响压裂水平井产能的主控因素;随机森林模型的预测效果最好,预测准确度达到94%,测试集均方根误差为0.934;三种树形模型中的随机森林模型优于决策树模型和梯度提升决策树模型,解决了其他树形模型的过拟合问题。

多分量裂缝预测技术研究及其在川南页岩气工区的应用

与常规油气相比,页岩中普遍含气,但是单井的产量变化大,通过改造形成裂缝网络的页岩气井才具有工业开发价值。目前,页岩裂缝预测的方法较多,大多数是基于纵波地震资料的,而利用多分量地震资料预测裂缝发育区仍是研究人员探索和努力的方向。这里首次运用转换横波地震资料对页岩气的裂缝发育情况进行预测研究。首先,利用转换波模型正演,结合多波地震资料对比识别页岩的小断裂、小断层。其次,通过基于横波分裂特性的裂缝预测技术对高角度的裂缝走向和密度进行预测。通过四川盆地实际资料应用表明,多分量裂缝预测技术能够有效识别页岩气的裂缝发育情况,多分量裂缝预测效果明显优于纵波叠前裂缝预测,成果更加合理。