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.

Identification of watered-out zones in carbonate reservoirs using resistivity curve reconstruction based on reservoir classification

The KT-II layer in the Zananor Oilfield,Caspian Basin,Kazakhstan,contains carbonate reservoirs of various types.The complex pore structure of the reservoirs have made it difficult to identify watered-out zones with traditional logging interpretation methods.This study classifies the reservoirs on the basis of core analysis and establishes an identification model for watered-out layers in the field to effectively improve the interpretation accuracy.Thin section analysis shows that there are three types of pores in the reservoirs,i.e.,the matrix pore,fracture and dissolution vug.A triple porosity model is used to calculate the porosities of the reservoirs and the results are combined with core analysis to classify the reservoirs into the fractured,matrix pore,fracture-pore as well as composite types.A classification standard is also proposed.There are differences in resistivity logging responses from the reservoirs of different types before and after watering-out.The preewatering-out resistivities are reconstructed using generalized neural network for different types of reservoirs.The watered-out layers can be effectively identified according to the difference in resistivity curves before and after watering-out.The results show that the watered-out layers identified with the method are consistent with measured data,thus serving as a reference for the evaluation of watered-out layers in the study area.

Pore structure effect on reservoir electrical properties and well logging evaluation

The reservoir pore structure controls the reservoir quality and resistivity response of hydrocarbon-bearing zones and thus, critically affects logging interpretation. We use petrophysical data in three types of reservoir with different pore structure characteristics to show that the complexity of pore structure had a significant effect on the effective porosity and permeability regardless of geological factors responsible for the formation of pore structure. Moreover,, the distribution and content of conductive fluids in the reservoir varies dramatically owing to pore structure differences, which also induces resistivity variations in reservoir rocks. Hence, the origin of low-resistivity hydrocarbon-bearing zones, except for those with conductive matrix and mud filtrate invasion, is attributed to the complexity of the pore structures. Consequently, reservoir-specific evaluation models, parameters, and criteria should be chosen for resistivity log interpretation to make a reliable evaluation of reservoir quality and fluids.

Identification and Evaluation of Low Resistivity Pay Zones by Well Logs and the Petrophysical Research in China

This paper presents an overview of petrophysical research and exploration achievements of low resistivity pay （LRP） zone by well logs in China. It includes geological characteristics and characteristics of well log response of the low resistivity pay zones discovered and evaluated in recent years, as well as the problems in recognizing and evaluating low resistivity pay zones by well logs. The research areas mainly include the Neogene formations in the Bohai Bay Basin, the Triassic formations in the northern Tarim Basin and the Cretaceous formations in the Junggar Basin, The petrophysical research concerning recognition and evaluation of the low resistivity pays, based on their genetic types, is introduced in this paper.

A new methodology for identification of potential pay zones from well logs: Intelligent system establishment and application in the Eastern Junggar Basin, China

In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years＇ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system （IPIS）. Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content （Vsh）, numbered rock type （RN）, porosity （φ）, permeability （K）, true resistivity （RT） and spontaneous-potential （SP）. Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks （ANNs）. Finally, all the six parameters are input into a neuro-fuzzy inference machine （NFIM） to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system＇s accuracy and effectiveness.

Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data

In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.

鄂尔多斯盆地志丹地区长2段低阻油层特征及油气地质意义

鄂尔多斯盆地志丹地区延长组长2段油层的电阻率普遍偏低,导致油层识别难度大,而长期作为非主力开发层系。围绕低阻油层存在的测井识别困难问题,通过对岩心、地层水、铸体薄片、黏土矿物、测井曲线、生产动态等资料的综合处理,分析地层水矿化度、微观孔隙结构、岩石矿物成分等典型特征对油层电阻率降低的影响,探讨研究区低阻油层勘探与开发的油气地质意义。结果表明:受三角洲平原亚相沉积背景控制,延长组长2段以中-细粒长石砂岩为主,在差异压实作用下储层孔隙类型多样;志丹地区地层水矿化度较高,并伴随微观孔隙结构复杂、黏土附加导电作用强等特征,流体与岩石构成的自由导电网络成为油层低阻的内在原因;长2段低阻油层是一类极具挖潜优势的隐蔽性油藏,将交会图法与邻近水层对比结果相结合,可以有效提高测井识别符合率。建议加强延长组长2段低阻油层的成因分析及测井识别研究,为延长油田老油区稳产挖潜提供理论依据。

四川盆地华蓥山断裂带南段地应力方向的差异:来自钻孔成像测井的启示

华蓥山断裂带作为四川盆地内规模最大的断裂带,两侧地质构造存在较大差异,历史上该断裂带上频发5级左右地震,现今仍具一定活动性,扰动着盆地的地应力场。明确华蓥山断裂带的地壳应力状态可以提高对该地区活动变形、构造活动以及地震活动性的认识,并对后续华蓥山断裂带研究提供参考依据。在收集整理华蓥山断裂带南段8处钻孔测井成像(钻孔崩落与钻孔诱发张裂缝)数据基础上,结合中国现代地应力场和四川盆地东南缘的地震震源机制解数据对其地应力特征进行了综合分析。研究结果表明:华蓥山断裂带南段南部地区大多数钻孔的最大水平主应力为北西西—南东东向,与四川盆地区域应力场方向一致,仅1处钻孔的最大水平主应力呈北东东—南西西向;在南段中部地区钻孔的最大水平主应力均为北东东—南西西向,对比四川盆地区域应力场呈逆时针偏转。应力方向发生偏转主要是由华蓥山断裂带基底性质变化、构造应力和区域应力场的综合作用造成的。

榆神矿区工作面煤层顶板导水裂隙带特征

为探测导水裂隙带发育特征及高度,以榆神矿区西南部30201工作面为研究对象,采用钻孔水文观测、岩芯地质、物理测井、直流电法、密集分布式光纤感测相结合的导水裂隙带探测技术以及数值模拟,对导水裂隙带发育高度及特征进行研究。结果表明:工作面裂隙带发育高度为135.00 m,裂采比为21.43,冒落带发育高度为66.38 m,冒采比为10.5,弯曲下沉带高度为94.87 m;呈现出沿工作面倾向导水裂隙带在巷道发育最小,在工作面中心位置发育最大的“拱形”形态,冒落带在工作面中心位置发育最大,在巷道位置发育最小的特征;数值模拟精度与建立模型的地质条件参数密切相关,数值模拟与现场实测结果基本吻合。

基于特征融合的微电阻率成像测井空白带无监督填充方法

针对微电阻率电成像测井仪器的特点导致测井成像呈现规律性空白带的问题,本文提出一种融合多尺度多层级特征的无监督填充模型及全井段填充框架用于填充空白带。填充模型采用UNet架构,利用非空白带区域电阻率数据自身的统计先验基于MAE损失进行无监督训练填充,主要通过以下2个措施对传统UNet进行改进:在编码器中引入MSR-Conv,提升单层网络的多尺度表征能力;在编解码特征连接环节引入多层级编码特征融合模块与信息引导模块,丰富上采样的特征尺度,减少解码过程中的信息丢失。实验结果表明:相较UNet,本文所提模型在自然场景数据集上的视觉效果与客观指标均有明显提升,其中PE降低了19.03%,SSIM提升了2.9%,PSNR提升了4.66%。全井段填充框架应用填充模型分段训练填充空白带电阻率数据后再合并,实现端到端填充单口井的微电阻率成像测井空白带,填充结果具有一定的鲁棒性,贴合实际生产场景。

基于多关键井平均曲线标准化方案的致密砂岩渗透率精确计算

X地区致密砂岩储层地质条件复杂,纵横向非均质性强;测井资料采集时间跨度大、标准化程度低;储层参数影响因素复杂,导致孔隙度、渗透率的计算精度低。为此提出一种多关键井平均、双标准层控制、趋势面法与直方图法相互验证的测井曲线标准化方案,系统解决单一标准层存在的相变、缺失、非均质性、特征值异常等一系列问题,标准化后孔隙度计算精度显著提高。在此基础上,基于流体流动单元储层分类理论,确立储层分类标准,构建基于流体流动单元的分类渗透率精确计算模型。利用多元线性回归方法建立测井曲线表征的流体流动单元计算模型,实现储层分类和渗透率计算的连续、定量评价。提出的标准化方案可提高测井曲线标准化的稳定性、适应性和可靠性,为储层分类及参数计算提供坚实的数据基础。该方法为该地区致密储层精细评价提供有力的技术支持,具有重要的应用和推广价值。

倾角测井资料在冷湖地区含气储层评价中的应用

柴达木盆地北缘山前带存在着一套以侏罗系煤系烃源岩为主的含气储层,沿着冷湖构造带呈条带状展布,已成为天然气勘探的重要领域。目前,关于冷湖地区气层解释评价研究往往集中在岩性及孔隙结构评价方面,针对含气储层与浅表滑脱断层的关系研究相对匮乏。利用地层倾角资料解释处理,首先对比四类软件进行处理参数优选,然后综合地震剖面及测录井资料,多井对比明确了含气储层与断裂带的关系,研究揭示了含气显示集中段主要分布在滑脱断层带下盘,且距离断点位置越近,具有含气性越好的特征,这一认识有效支持了老井复查挖潜工作并取得实效。

断裂构造中电阻率与声波波速特征分析及其在探测过程中的联合运用

查明断裂构造的具体位置对于工程场址的选择和稳定性评价具有重要意义。高密度电阻率法和声波测井是广泛运用于探测断裂构造的地球物理方法。高密度电阻率法具有对低阻体敏感且探测范围较大的特点,但高密度电阻率法的分辨率有限,难以准确圈定地质体的边界位置;声波测井具有准确识别地层的特点,但其横向探测范围非常有限。为了克服上述问题,结合2种方法在断裂构造探测中各自的优势以提高圈定地质体的精度,文章首先通过物性试验证明岩石破裂前后电阻率与声波波速变化的相关性,随后用一实例分析断裂构造的电阻率与声波波速特征以及两者在探测过程中的联合运用,最后借助声波测井数据对高密度电阻率法反演结果进行深度校正,得到断裂构造在地下空间的三维分布位置。结果表明:岩石破裂后的电阻率与声波波速较未破裂前均偏低;断裂构造的破碎带在高密度电阻率法反演剖面上以低阻异常区域存在,在声波波速曲线上破碎带区域的波速值会有明显的骤降,2种方法的组合探测模式可为断裂构造具体位置的圈定提供更好的指导。钻探结果验证了借助声波测井数据校正后的高密度电阻率法反演结果比未校正之前更加符合实际地质情况。

花岗岩地区地铁风井深基坑工程地质条件研究

为研究区域断层构造影响带内的深基坑工程地质条件,以“面→线→点”的总体思路,采用地质调查、地面物探、孔内物探、机动钻探、岩土测试等“五位一体”的综合勘察手段,查明基坑范围内的断层产状及宽度、地层分布规律、岩土体力学性质等,归纳适用于复杂地质条件的深基坑工程的勘察方法。首先,开展地质调查,从“面”上查明基坑工程位于F2区域断层影响带内,发育3组交错节理,断层构造加剧了节理裂隙的发育程度,导致基坑范围内岩体破碎、差异风化严重;然后根据瞬变电磁法,从“线”上获得断层及影响带宽55~65 m,初步建立基坑结构与断层构造的空间关系;最后根据机动钻探及孔内电阻率测试,从“点”上揭示基坑纵向岩土体“软硬相间”的分布特征,地层受构造挤压严重,断层影响深度达60 m,岩体风化程度高、强度低,以散体状为主,间夹碎块状,空间上无规律性,表现为典型的断层构造带特征。研究表明,通过利用不同勘探手段的优势,相互验证、综合分析,可查明复杂地质条件下深基坑工程的地质条件和岩土力学特征,为设计方案提供可靠的地质依据。

基于测井约束反演的基岩风化带含水层富水性分析

反演是瞬变电磁资料解释的重要手段,而由于缺乏足够的高低频信息,常用的反演方法存在横向连续性差、纵向分辨率低、对数据的控制力差等缺陷,对地层的空间刻画能力较低。为了提高反演精度,基于Occam算法和测井曲线建立了约束地质模型,充分利用测井信息细化反演细节,以陕北某煤矿为研究对象,将传统光滑模型与测井约束模型进行对比。研究结果表明,2种模型与实测数据均有较高的拟合度,但光滑模型易受曲线尾支跳变点影响从而失去真实的地质信息;测井约束模型能充分利用区内各地层电性特征从而减少突变,使得横向连续性增强,同时对于纵向分辨率也有一定提高,反演结果与实际情况吻合,可作为反演常用手段以提高资料解释精度。

低阻水淹层地化录井识别技术在南海北部A油田的应用

在油田开发过程中,常采用注水方式为地层提供能量。由于储层的非均质性,通常物性好、连通性好的层位被快速水淹,剩余油较少,而物性差、连通性差的层位因注入水未波及,则留存较多剩余油。部署调整井开发剩余油需避开水淹层射孔,因此需要对水淹层进行准确识别。通过优选S_(1)、nC_(24)/nC_(29)、∑(C_(1)-C_(5))/∑(C_(1)-C_(9))等地化录井优势参数进行有机组合,建立了基于地化谱图对比识别方法、热蒸发烃气相色谱参数nC24/nC29比值法,以及轻烃组分∑(C_(1)-C_(5))/∑(C_(1)-C_(9))比值与岩石热解S1交会图板法用于低阻水淹层地化录井识别。该技术在南海北部海域A油田的应用表明,水淹层识别符合率达到87.5%,取得了较好的应用效果,为油田开发避开水淹层提供了科学依据。

GIS-based Risk Zone of Flood Hazard in Anhui Province

[Objective] The aim was to study the flood disaster risks in Anhui Province based on GIS. [Method] Taking country as basic unit, the 1∶ 250 000 basic geographic data in Anhui Province as basis, from the angle of flood disaster hazard and economic vulnerability, and by dint of the calculation of the weight of each impact factor with entropy-based fuzzy AHP method, flood risk assessment model was established to study the flood disaster risks zoning in Anhui Province. Using nearly 10 years of disaster information in Anhui Province, the flood risk zoning of Anhui Province was studied. And the risks evaluation results of flood disaster risks in Anhui Province in recent 10 years were checked. [Result] The regional difference of flood disaster in Anhui Province was large. The most serious area of flood disaster was in Lingquan in Fuyang and Lingbi in Huaibei. The risks degree degraded from south mountainous area in north Anhui Plain to the mountainous area of west Anhui Province, from Huaibei Plain to the hilly area of Jianghuai and mountainous area of south Anhui Province. The disaster situation in Anhui Province in recent 10 years suggested that the areas suffering from serious economic losses were in Lingbi, Guzheng and Huainan in the south part of Huaibei Plain. The places having serious agricultural crops damages were in Tangshan and Xiao County in Huaibei Plain. Besides, the Jingzhai area in the Dabieshan in west Anhui Province also had serious agricultural crops in Jinzhai. Other places had limited disaster-stricken impacts; the distribution of disaster-stricken population and impacted area of agricultural crops were basically consistent. Therefore, the risk evaluation of flood disaster of Anhui Province based on GIS was basically consistent with reality. [Conclusion] This GIS-based flood risk zoning method had good practicability.

Investigation on influence factors of dual laterologs curve form

In order to investigate the influences of caliper, formation thickness and invaded zone on the form of dual laterologs, forward modeling technique were applied to calculate the dual laterologs for different cases. The result shows that the resistivity logs become smoother and lower as the borehole diameter increases, the increase of the contrast between mud resistivity and formation resistivity induce the logs to be more pointed. When the formation thickness is less than lm, the two-peak on the logs for resistive invasion vanished, and for thickness between 1 m and 4 m, the form of logs does not vary significantly. If the formation thickness is greater than 4 m, a platform appears on the logs at the middle of the formation. The thinner the invaded zone is, the more obvious the invasion feature on the laterologs is. For thick invaded zone the form of logs tend to be that of an uninvaded resistive formation. The form and amplitude of logs depend on the resistivity contrast between invaded zone, uninvaded formation and adjacentlayers.

顺北油气田奥陶系断控体结构模式研究

顺北油气田奥陶系目的层埋深普遍在8000 m左右,三维地震资料采集面元25 m×25 m,地震资料分辨率较低,导致顺北油气田奥陶系鹰山组断控体油气藏难以精细刻画与表征。结合常规测井、电成像、远探测、取芯等资料,开展不同应力作用下断控体结构分析、断控体油气藏破碎裂缝带、破碎角砾带、断裂空腔分布研究。结果表明:断控体油气藏主要储集空间来自多期构造活动叠加改造形成的断裂空腔、角砾间孔隙、构造裂缝,不同应力机制决定断控体的发育程度,拉张背景易形成断裂空腔及裂缝带,挤压背景易形成破碎角砾带及裂缝带;断控体常发育典型的核带结构,裂缝带的宽度范围较大(0.35~1.64 km),且分布范围广,角砾带的宽度范围次之(0.44~0.93 km),且分布范围中等,断裂空腔的宽度范围最小(0.25~0.35 km),且分布范围小,主要集中在局部拉张应力区。