A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372,New Zealand

Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore stability.However,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex geo-processes.To overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical methods.The empirical method for PP prediction is comprised of data pre-processing and model establishment stage.Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372.Gamma-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction.The normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone data.The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development.The dataset is prepared and divided into two parts:Training and Testing.The PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression model.The Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

Characterization of reservoir properties and pore structure based on micro-resistivity imaging logging: Porosity spectrum, permeability spectrum, and equivalent capillary pressure curve

According to the capillary theory,an equivalent capillary model of micro-resistivity imaging logging was built.On this basis,the theoretical models of porosity spectrum(Ф_(i)),permeability spectrum(K_(i))and equivalent capillary pressure curve(pe)were established to reflect the reservoir heterogeneity.To promote the application of the theoretical models,the Archie's equation was introduced to establish a general model for quantitatively characterizing bi,K,and pei.Compared with the existing models,it is shown that:(1)the existing porosity spectrum model is the same as the general equation of gi;(2)the Ki model can display the permeability spectrum as compared with Purcell's permeability model;(3)the per model is constructed on a theoretical basis and avoids the limitations of existing models that are built only based on the component of porosity spectrum,as compared with the empirical model of capillary pressure curve.The application in the Permian Maokou Formation of Well TsX in the Central Sichuan paleo-uplift shows that the Ф_(i),K_(i),and p_(ci) models can be effectively applied to the identification of reservoir types,calculation of reservoir properties and pore structure parameters,and evaluation of reservoir heterogeneity.

A new method to construct reservoir capillary pressure curves using NMR log data and its application

By analyzing hundreds of capillary pressure curves, the controlling factors of shape and type of capillary pressure curves are found and a novel method is presented to construct capillary pressure curves by using reservoir permeability and a synthesized index. The accuracy of this new method is verified by mercury-injection experiments. Considering the limited quantity of capillary pressure data, a new method is developed to extract the Swanson parameter from the NMR T2 distribution and estimate reservoir permeability. Integrating with NMR total porosity, reservoir capillary pressure curves can be constructed to evaluate reservoir pore structure in the intervals with NMR log data. An in-situ example of evaluating reservoir pore structure using the capillary pressure curves by this new method is presented. The result shows that it accurately detects the change in reservoir pore structure as a function of depth.

A formation pressure prediction method based on tectonic overpressure

Traditional formation pressure prediction methods all are based on the formation undercompaction mechanism and the prediction results are obviously low when predicting abnormally high pressure caused by compressional structure overpressure.To eliminate this problem,we propose a new formation pressure prediction method considering compressional structure overpressure as the dominant factor causing abnormally high pressure.First,we establish a model for predicting maximum principal stress,this virtual maximum principal stress is calculated by a double stress field analysis.Then we predict the formation pressure by fitting the maximum principal stress with formation pressure. The real maximum principal stress can be determined by caculating the sum of the virtual maximum principal stresses.Practical application to real data from the A1 and A2 wells in the A gas field shows that this new method has higher accuracy than the traditional equivalent depth method.

A Study on the Classification and Well-Logging Identification of Eclogite in the Main Hole of Chinese Continental Scientific Drilling Project

Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating density and neutron well-logging, and so on. In this study, according to the diversities of minerals and chemical components and well-logging responses, edogites are classified from three aspects of origin, content of oxygen, and sub-mineral. We studied the logging identification method for eclogite sub-classes based on multi-element statistics and reconstructed 11 kinds of eclogite. As a result, eclogites can be divided into 6 types using well logs. In the light of this recognition, the eclogite in the main hole is divided into 20 sections, and the distribution characters of all sub-classes of eclogite are analyzed, which will provide important data for geological research of CCSD.

Clay Minerals Properties as Downhole Formation Pressure Indicator

The successful estimation of formation pressures （or formation pore gradient） is fundamental and the basis for many engineering works including drilling and oilfield development planning. Common log data are used for formation pressure calculation. Modern techniques for pressure prediction have several disadvantages, notably, incorrect account of the downhole nonsteady thermal field and clay mineral composition. We propose a way to overcome listed shortcomings： a technique for thermal field proper account while formation pressure estimation and a petrophysical model, which reflects relationships between clay minerals composition and rock properties, derived from log data.

Neutron and density logging responses to gas reservoir for well-balanced and under-balanced logging:Gas reservoirs of sandstone in a western China field

Studying the response differences between neutron and density logging of gas reservoir for well-balanced and under-balanced logging will be of significance in evaluation of gas reservoir under the under-balanced condition and application of logging data. With Monte Carlo simulation technique,the paper obtains the relationship between neutron and density logging measurement and borehole di-ameter,porosity or gas saturation for well-balanced and under-balanced logging. The conclusions show that the response trend of under-balanced logging to gas reservoirs agrees with that of well-balanced logging with small borehole,and under-balanced logging data can be used usually as well-balanced logging data. When borehole diameter is large,under-balanced logging data should be corrected for the influences of borehole.

基于神经网络模型的水平井破裂压力预测方法

破裂压力是井身结构设计的基础依据,也是水力压裂设备选型和方案设计的基础参数,通常采用测井解释获取破裂压力剖面,但其存在参数准确获取难、计算过程繁琐、普适性较差、计算精度低等问题,机器学习提供了一种解决这些问题的新方法。为此,以测井数据作为输入参数,采用4种不同的神经网络模型,建立水平井测井数据与破裂压力间的非线性关系,通过测试集预测结果的对比分析,优选出最佳的神经网络模型,并优化模型网络结构和超参数,实现水平井破裂压力的直接预测。研究结果表明:1)破裂压力与井斜角、横波时差和纵波时差表现为极强相关性,与井深、岩性密度和补偿中子表现为强相关性,与井径和自然伽马表现为弱相关性;2)不同组合的测井参数对模型预测结果具有显著影响,最优输入参数为井斜角、横波时差、纵波时差、井深、岩性密度和补偿中子;3)对比多层感知机、深度神经网络、循环神经网络和长短期记忆神经网络(LSTM)模型,发现LSTM模型的预测效果最佳;4)优化了LSTM模型的网络结构及超参数,优化后破裂压力预测的平均绝对百分比误差为0.106%、决定系数为0.996。LSTM模型能够有效构建水平井测井参数与破裂压力之间的非线性关系,可以实现水平井破裂压力的准确预测,对于准确预测破裂压力、简化破裂压力计算过程、推广机器学习在石油工程领域的应用具有重要的作用。

基于核磁共振测井T2谱评价储层孔隙结构仿真实验设计

储层孔隙结构评价对油气资源勘探开发和二氧化碳地质封存等具有重要意义。将核磁共振测井横向弛豫时间(T2)谱转换为毛管压力曲线是进行储层孔隙结构评价的有效手段。针对转换模型理论性强、原理抽象复杂等特点,利用MATLAB GUI设计了基于核磁共振测井T2谱评价储层孔隙结构的仿真实验。该交互界面包含毛管压力曲线数据预处理、核磁共振T2谱转换毛管压力曲线及孔隙结构特征参数提取等模块。通过在仿真实验中利用不同转换方法将核磁共振T2谱转换为毛管压力曲线,并将处理结果与实验测量数据在图形界面进行比对,最终从转换所得毛管压力曲线中提取孔隙结构特征参数,进行储层孔隙结构评价。该仿真实验操作简单,能将不同方法的处理过程与结果直观展示给学生,增强学生对不同转换模型预测毛管压力曲线的理解与认识,提高了地球物理测井教学效果。

基于斑马算法优化支持向量回归机模型预测页岩地层压力

针对陇东地区三叠系延长组7段(长7段)页岩孔隙结构复杂、非均质性强、地层压力预测精度较低等问题,提出了一种基于斑马算法优化支持向量回归机(ZOA-SVR)模型预测地层压力的方法,并在实际钻井中进行了应用,将预测结果与基于机器算法的模型和常规地层压力预测方法结果进行了对比。研究结果表明:①ZOA-SVR模型以实测地层压力数据为目标变量,优选与陇东地区长7段页岩地层压力数据关联度达到0.70以上的深度、声波时差、密度、补偿中子、自然伽马、深侧向电阻率、泥质含量等7个参数作为输入特征参数,设置训练样本数为40,交叉验证折数为5,初始化斑马种群数量为10,最大迭代次数为70,对惩罚因子和核参数进行优化并建模,参数优化后拟合优度指标R2达到0.942,模型预测的地层压力数据在训练集和测试集上的绝对误差均低于1 MPa,预测测试集地层压力数据与实测压力数据的平均相对误差为2.42%。②ZOA-SVR模型在研究区长7段地层压力预测中优势明显,比基于粒子群优化算法、灰狼算法和蚁群算法的模型具有更好的参数调节及优化能力,R2分别提高了0.209,0.327,0.142;比等效深度法、Eaton法、有效应力法预测的地层压力精度更高,相对误差分别降低了32.53%,15.31%,5.91%。③ZOA-SVR模型在实际钻井中的应用结果显示,研究区长7段地层压力在垂向上分布较稳定,泥页岩段的地层压力高于砂岩段,地层压力系数主要为0.80~0.90,整体上属于异常低压环境,与实际地层情况相符。

库车坳陷深层盐下白垩系储集层氯盐分布模式及意义

库车坳陷克拉苏构造带盐下白垩系巴什基奇克组储集层分布了一批高产、稳产的天然气藏群,其储集层为超深、高温、超压致密砂岩储集层,孔隙度越高,氯盐含量越高,储集层视电阻率越低,储集层中氯盐的分布严重影响流体识别,其对盐下致密砂岩物性的影响较明显。利用岩心观察、铸体薄片、扫描电镜、氯盐含量、常规测井曲线分析等,系统分析了库车坳陷盐下白垩系巴什基奇克组储集层氯盐的分布特征。根据氯盐含量、电阻率、氯盐来源等的差异,提出盐下储集层氯盐分布具有顶渗、侧渗和局部封存3种模式。顶渗模式与侧渗模式的电阻率仅受氯盐含量的影响,顶渗模式储集层氯盐含量呈垂向分带特征,随氯盐含量的降低,电阻率升高;侧渗模式储集层氯盐含量呈横向分带特征,电阻率自构造带边缘向中心表现出由高到低再升高的趋势;局部封存模式电阻率受应力和氯盐含量的共同影响,氯盐含量分布具有偶发性,电阻率变化幅度大。根据测井响应特征划分了各模式的分布段序列,顶渗模式自上而下发育盐层段、泥岩封隔段、饱和氯盐强影响段、未饱和氯盐强影响段、未饱和氯盐影响过渡段和氯盐未影响段;侧渗模式在顶渗模式基础上多发育过饱和氯盐影响段;局部封存模式从上到下划分为盐层段、泥岩封隔段、强挤压应力氯盐未影响段、氯盐应力混合影响段和氯盐应力未影响段。

准噶尔盆地南缘异常高压泥岩段地层压力预测方法

准噶尔盆地南缘安集海河组泥岩层段为异常高压地层,但地层异常高压的成因机制尚不清晰。此外,该泥岩层段具有强流体敏感性,易造成测井信息产生偏差,不利于地层压力预测。为此,考虑流体作用对泥岩物性参数的影响,建立了测井物性校正方法。在此基础上,综合声速-密度曲线与综合压实曲线,揭示了泥岩地层异常高压成因机制,融合有效应力理论与多元回归分析方法,构建了泥岩段多参数地层压力预测方法。研究表明:准噶尔盆地南缘泥岩地层高压成因机制为欠压实、构造挤压以及构造压实;测井校正后,随有效应力增加,泥岩纵波时差、孔隙度、自然伽马和平均水平应力减小,密度增大;有效应力与纵波时差、密度、孔隙度呈线性关系,与自然伽马、平均水平地应力呈指数相关。以此为依据,构建了泥岩地层压力测井预测方法,验证了其适用性。研究成果有助于研究区泥岩段钻井方案设计,对准噶尔盆地南缘区域油气高效勘探开发具有重要意义。

基于三维地质力学的压力剖面钻前预测研究

为解决目前基于邻井测井资料评价得到的压力剖面与待钻井实际井轨迹上的结果不符等问题,提出基于三维地质力学的待钻井井眼轨迹上的压力剖面钻前预测方法。以地震解释的层位数据为基础,结合断层数据、钻井分层数据等构建三维地质模型。然后,利用已钻井测井数据,通过序贯高斯插值法获得三维岩石力学参数场,并且通过有限元数值模拟得到区域地应力以及压力系统,进而获得待钻井井眼轨迹上的压力剖面。研究结果表明:该方法预测精度较高,已钻井的孔隙压力预测值与实测值的误差为3.57%,地应力预测值与实测值误差为5.8%。现场2口井的预测层位与实钻层位吻合程度较高,预测的三压力剖面能够较好地解释实钻过程中出现的井下复杂问题。研究结果可为钻前地质工程设计、井身结构优化、井下复杂问题预防等提供较为可靠的数据参考。

基于多对数螺旋曲线破坏机构的富水软岩隧道开挖面支护应力分析

为了避免对胡克-布朗准则进行任何线性化处理,将广义胡克-布朗(generalized Hoek-Brown, GHB)强度准则直接引入到开挖面稳定性分析中,基于极限分析理论构建了富水地层浅埋隧道开挖面多对数螺旋线破坏机构。在考虑孔隙水压力的作用下,推导出能量平衡方程,得到开挖面极限支护应力表达式,并且通过程序优化得到支护应力最优上限解。在此基础上,分析了GHB强度参数、孔隙水压力系数、岩土体重度以及隧道埋深比对开挖面稳定性的影响。最后通过归一化处理得到不同情况下的开挖面极限支护应力稳定性图表,通过该图表能够快速便捷地获得富水地层隧道开挖面维持稳定性所需的极限支护应力,为盾构隧道初步设计提供理论依据。

基于气测录井的高温高压地层压力随钻监测技术

基于气测录井地层压力监测基本原理,给出高温高压地层钻井液静态当量泥浆密度(ESD)和钻井液循环当量泥浆密度(ECD)的计算方法,并在此基础上对高温高压井地层压力进行随钻监测。研究发现,当井底压差增大时气测值呈指数式降低,且当井底压力处于过平衡状态时气测值不会降至0,而是保持一个较低的数值水平。据此分析泥浆循环气与循环压差以及单根气与静压差的相关关系,建立井底压差与气测值的相关方程,进而对地层压力作出定性和定量评价。

测井仪器传热理论研究及仿真试验分析

为了保障石油开采向万米深层进发,提高测井仪器的耐温耐压指标,解决测井仪器电路热失效问题,针对测井仪器开展传热理论研究及仿真试验分析。根据测井仪器实际工况,通过仪器与井下流体的对流模型分析不同因素对平均对流换热系数的影响;针对直肋散热器构建传热模型,计算肋片散热效率,通过有限元方法求解测井仪器三维模型,并开展仪器高温试验,分析不同类型散热器对芯片温度的影响。结果表明,流体的平均对流换热系数与仪器上提速度正相关,可以通过调整井下流体成分的配比改变平均对流换热系数;测井仪器高温区域主要集中在芯片附近,应考虑将热量向仪器两端传递以提高散热效率;高温试验测试结果与散热器仿真结果一致,均说明弧形肋片散热器的散热效果最优。该研究可以为测井仪器热管理提供理论指导。

Reservoir quality of the Late Cretaceous Volador Formation of the Latrobe group,Gippsland Basin,Australia:Implications from integrated analytical techniques

Increasing demand for energy due to the populous Eastern Australia has driven oil and gas industries to find new sources of hydrocarbons as they are the primary energy suppliers.Intensive study has been done on the Volador Formation in the Gippsland Basin by means of core-based petrophysical,sedimentological,and petrographic analyses as well as well log-based interpretation and capillary pressure test.Five wells from Kipper,Basker and Tuna fields with available dataset were investigated in this study:Kipper-1,Basker-1,Basker-2,Basker-5 and Tuna-4.Overall,the formation has good reservoir quality based on the high porosity and permeability values obtained through core and well log petrophysical analyses.The formation made up of mostly moderate to coarse quartz grains that has experienced strong anti-compaction and is poorly cemented.Montmorillonite and illite clays are seen dispersed in the rock formation,with the minority being mixed clays.These clays and diagenetic features including kaolinite cement and quartz overgrowth that can lead to porosity reduction only have insignificant impact on the overall reservoir quality.In addition,capillary pressure data shows that most samples are found in the transition to good reservoir zones(<50%saturation).The results obtained from this study have shown that the Volador Formation in the Gippsland Basin is worth for hydrocarbon exploration.

多因素影响下的气测数据综合校正技术

气测录井是油气勘探开发中一种必不可少的录井检测分析手段,但是由于多种因素影响,气测数据往往不能准确反映地下油气信息。在确定影响气测数据主要因素的基础上,建立钻头直径、钻时、钻井液排量、钻井液粘度以及井筒压差各单项影响因素校正模型,进一步利用主成分分析方法建立了气测数据多因素影响下的综合校正模型。通过辽河油区T 281井的应用,气测全烃数据质量有了较明显的提升,能够更好地反映地层真实的含油气信息,经试油验证该技术应用效果良好,可以有效增强气测数据的可对比性,提高了录井综合解释的精度,为油田高效勘探开发、施工决策及试油选层提供了有力的技术支撑。

岩矿扫描录井在碳酸盐岩储层异常高压预测中的应用

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