Total organic carbon content logging prediction based on machine learning:A brief review

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

Analysis on Variation of Soil Organic Carbon and Total Nitrogen Content and Carbon Storage in the Oasis Cotton Field of Manas River Valley

Objective] The research aimed to study soil organic carbon and total ni-trogen distribution in oasis cotton farmland. [Method] With the oasis cotton field of Manas River Val ey in Tianshan Mountains as the research area and abandoned farmland as a control, the distribution characteristics of soil organic carbon and total nitrogen content in the cotton field of Manas River Val ey in the last 23 years were investigated by using geographic methods. [Result] Presenting vertical distribution, cotton soil organic carbon and total nitrogen content in Manas River Val ey de-creased with the increase of soil depth, and those in 0-30 cm soil layer was sig-nificantly higher than those in soil layer of below 30 cm, while organic carbon stor-age showed the trend of increase. Also in vertical distribution, soil organic carbon and total nitrogen decreased significantly with the increase of soil depth, and soil organic carbon content in abandoned farmland decreased month by month. Howev-er, cotton soil organic carbon storage firstly decreased and then increased in the oasis cotton field that in the early growth of cotton, soil organic carbon in the layers of 0-30 and 30-100 cm decreased to the lowest in the bloom stage, and then or-ganic carbon increased with the reproductive growth of cotton into the later stages. However, due to no input of plant litter in the abandoned farmland, the soil organic carbon storage decreased month by month. There were significantly differences be-tween oasis cotton field and abandoned farmland in organic carbon contents. [Con-clusion] The soil organic carbon content and total nitrogen content in oasis cotton field were significantly higher than those in the abandoned farmland. The soil organ-ic carbon storage increased in the layer of 0-30 cm, while there was no significant change of soil organic carbon and total nitrogen content in the layer of 30-100 cm, which was consistent with the previous study on the distribution characteristics of soil organic carbon and total nitrogen content profile.

Geophysical prediction of organic matter abundance in source rocks based on geochemical analysis:A case study of southwestern Bozhong Sag,Bohai Sea,China

The Bozhong Sag is the largest petroliferous sag in the Bohai Bay Basin,and the source rocks of Paleogene Dongying and Shahejie Formations were buried deeply.Most of the drillings were located at the structural high,and there were few wells that met good quality source rocks,so it is difficult to evaluate the source rocks in the study area precisely by geochemical analysis only.Based on the Rock-Eval pyrolysis,total organic carbon(TOC)testing,the organic matter(OM)abundance of Paleogene source rocks in the southwestern Bozhong Sag were evaluated,including the lower of second member of Dongying Formation(E_(3)d2L),the third member of Dongying Formation(E_(3)d_(3)),the first and second members of Shahejie Formation(E_(2)s_(1+2)),the third member of Shahejie Formation(E_(2)s_(3)).The results indicate that the E_(2)s_(1+2)and E_(2)s_(3)have better hydrocarbon generative potentials with the highest OM abundance,the E_(3)d_(3)are of the second good quality,and the E_(3)d2L have poor to fair hydrocarbon generative potential.Furthermore,the well logs were applied to predict TOC and residual hydrocarbon generation potential(S_(2))based on the sedimentary facies classification,usingΔlogR,generalizedΔlogR,logging multiple linear regression and BP neural network methods.The various methods were compared,and the BP neural network method have relatively better prediction accuracy.Based on the pre-stack simultaneous inversion(P-wave impedance,P-wave velocity and density inversion results)and the post-stack seismic attributes,the three-dimensional(3D)seismic prediction of TOC and S_(2)was carried out.The results show that the seismic near well prediction results of TOC and S_(2)based on seismic multi-attributes analysis correspond well with the results of well logging methods,and the plane prediction results are identical with the sedimentary facies map in the study area.The TOC and S_(2)values of E_(2)s_(1+2)and E_(2)s_(3)are higher than those in E_(3)d_(3)and E_(3)d_(2)L,basically consistent with the geochemical analysis results.This method makes up the deficiency of geochemical methods,establishing the connection between geophysical information and geochemical data,and it is helpful to the 3D quantitative prediction and the evaluation of high-quality source rocks in the areas where the drillings are limited.

提高煤系页岩储层TOC计算精度的改进方法

总有机碳含量(TOC)是决定烃源岩质量的最重要参数。为了准确评价煤系页岩气储层的TOC参数,以四川盆地Q区龙潭组C1井海陆过渡相页岩气储层为例,对提高储层TOC参数计算精度的方法进行了研究。根据研究区的地质特点,结合自然伽马能谱测井数据,对原始Δlog R方法进行改进,形成了基于自然伽马能谱测井的双Δlog R方法。首先,将岩心TOC数据按岩性分为煤层和非煤层,然后,将自然伽马能谱测井数据(钍钾比(Th/K)和钍铀比(Th/U))分别与煤层TOC和非煤层TOC进行相关性分析,各自选择最高相关性的数据与Δlog R拟合,计算煤层和非煤层的TOC。结果表明,基于自然伽马能谱测井的双Δlog R方法和传统方法相比,C1井煤层和非煤层TOC计算相关性最高可以提高到0.78和0.85,煤层和非煤层TOC的绝对误差最低都可以降到0.01%,煤层和非煤层TOC的相对误差降低到14.93%和12.53%。将基于自然伽马能谱测井的双Δlog R方法应用于四川盆地S区Y组C2井,取得了较好的效果。改进的方法适用于研究区页岩气储层黏土含量高,非均质性强,且存在黑色煤层的页岩储层,有效弥补TOC与铀(U)含量无明显相关性的不足,具有较好的适用性和推广性,可以有效辅助研究区页岩气储层的勘探开发。

Effects of degradation succession of alpine wetland on soil organic carbon and total nitrogen in the Yellow River source zone,west China

Wetland is an important carbon pool,and the degradation of wetlands causes the loss of organic carbon and total nitrogen.This study aims to explore how wetland degradation succession affects soil organic carbon(SOC)and total nitrogen(TN)contents in alpine wetland.A field survey of 180 soilsampling profiles was conducted in an alpine wetland that has been classified into three degradation succession stages.The SOC and TN contents of soil layers from 0 to 200 cm depth were studied,including their distribution characteristics and the relationship between microtopography.The results showed that SOC and TN of different degradation succession gradients followed the ranked order of Non Degradation(ND)>Light Degradation(LD)>Heavy Degradation(HD).SWC was positively correlated with SOC and TN(p<0.05).As the degree of degradation succession worsened,SOC and TN became more sensitive to the SWC.Microtopography was closely related to the degree of wetland degradation succession,SWC,SOC and TN,especially in the topsoil(0-30 cm).This result showed that SWC was an important indicator of SOC/TN in alpine wetland.It is highly recommended to strengthen water injection into the wetland as a means of effective restoration to reverse alpine meadow back to marsh alpine wetland.

储存条件对总有机碳(TOC)分析校准用蔗糖溶液的影响

为了提高总有机碳(total organic carbon,TOC)分析仪校准用蔗糖溶液定值的准确性,消除影响总有机碳含量的干扰因素,配制拟商业化标准系列溶液,本研究进行了不同水质、不同储存温度、不同材质储存器皿、湿度条件下低浓度蔗糖溶液的稳定性试验和加速实验以及3个批次拟商业化系列标准溶液的组间差异实验。结果显示,超纯水经高压灭菌后TOC含量变化不显著,呈最稳定状态;拧盖广口玻璃瓶储存低浓度蔗糖溶液的最佳温度范围是4~20℃。20℃时,蔗糖溶液中的TOC含量在拧盖聚四氟乙烯(Polytetrafluoroethylene,PTFE)瓶中比在聚丙烯瓶(polypropylen,PP)瓶中更稳定,且与玻璃瓶组无明显差异。加速实验结果显示,在高温(40±2°C)、高湿(75%±5%)条件下储存6个月,PTFE蔗糖溶液的TOC含量无显著变化。此外,随机抽取3个批次的拟商业化溶液并绘制标准曲线,获得了较好重现性和稳定性。通过分析可知,TOC含量会受水质、温度、储存器皿材质及时间等因素影响,选择PTFE能有效减少析出物,提高低浓度蔗糖溶液中TOC含量的稳定性。

Seasonal variations of organic carbon and nitrogen in the upper basins of Yangtze and Yellow Rivers

The profound impacts exerted by climate warming on the Tibetan Plateau have been documented extensively, but the biogeochemical responses remain poorly understood. This study was aimed at seasonal variations of total organic carbon(TOC) and total organic nitrogen(TON) in stream water at two gauging sections(TTH, ZMD) in the upper basin of Yangtze River(UBYA) and at fourgauging sections(HHY, JM, JG, TNH) in the upper basin of Yellow River(UBYE) in 2013. Results showed that concentrations of TON exhibit higher values in spring and winter and lower values in summer. TOC exhibits higher concentrations in spring or early summer and lower concentrations in autumn or winter. Seasonal variations of TOC and TON fluxes are dominated by water flux. In total, the UBYE and UBYA delivers 55,435 tons C of organic carbon and 9,872 tons N of organic nitrogen to downstream ecosystems in 2013. Although the combined flux ofTOC from UBYA and UBYE is far lower than those from large rivers, their combined yields is higher than, or comparable with, those from some large rivers(e.g. Nile, Orange, Columbia), implying that organic carbon from the Tibetan Plateau may exert a potentially influence on regional and/or global carbon cycles in future warming climate.

Correlation of Sillimanite &Kaliophilite Minerals, TOC, Ro, and MBT from Drill Cutting of Well BS-03 in the Development of Shale Hydrocarbon, Brownshale Formation, Bengkalis Trough, Central Sumatra Basin, Indonesia

Sillimanite is a brittle mineral as a metamorphic mineral product which is generally derived from clay, along with an increase in pressure and high temperature (600°C - 900°C), and kaliophilite is also a brittle mineral as a potassium bearing in the sand-shale series, which contributes to the clay diagenesis process. In the development of shale hydrocarbon in the Brownshale formation in the Bengkalis Trough, Central Sumatra Basin, using the correlation of the XRD (bulk and clay oriented), TOC, Ro, and MBT analysis results from the drill cuttings of well BS-03, so that the fracable zone interval can be determined. From this correlation, it shows that the presence of sillimanite and kaliophilite minerals as minor minerals greatly affects the changes in shale character and hydrocarbon generation, where at depth intervals of 10,780 ft downward (sand series-shale) there is an interesting phenomenon, i.e. low MBT, low TOC, and high Ro, so it is believed that the depth interval of 10,780 ft downward is a fracable zone interval (brittle shale) which is a good candidate for hydraulic fracking planning, while the upper depth interval is a fracture barrier.

基于Stacking算法集成学习的页岩油储层总有机碳含量评价方法

总有机碳含量(TOC)是页岩油储层评价的重要参数,而传统总有机碳含量测井评价方法精度较低且普适性较差,机器学习模型在一定程度上提高了总有机碳含量预测精度,但结果存在不稳定性。为了进一步提高页岩油储层总有机碳含量预测精度,基于有机质岩石物理特征和不同总有机碳含量测井响应特征的深入分析,优选出深侧向电阻率、声波时差、补偿中子和密度测井曲线作为总有机碳含量的敏感测井响应,并将其作为输入特征,以岩心分析总有机碳含量作为期望输出值,分别建立了决策树模型、支持向量回归机模型、BP(Back Propagation)神经网络模型,并建立了以决策树模型为基模型、支持向量回归机模型为元模型的Stacking算法集成学习模型。利用B油田A区块的岩心样本数据和实际井数据对不同模型预测总有机碳含量结果进行了验证,结果表明,基于Stacking算法的集成学习模型的总有机碳含量预测精度最高,相较于决策树模型、支持向量回归机模型、BP神经网络模型和改进的ΔlgR法,预测精度有较大提高。因此,基于Stacking算法的集成学习模型为该研究区最有效的总有机碳含量计算方法,这为准确地评估页岩油储层的生烃潜力、确保页岩油储层的高效开采及资源利用奠定了基础。

高纯液氨中痕量油分检测方法

介绍了气体中油分检测的多种方法,比较了各方法的原理与适用范围。根据液氨中油分杂质的特性,提出了用总有机碳分析仪对痕量油分进行分析的新方法,该方法检出限为0.0015 mg/kg。液氨中有机碳为0.01~1.0 mg/kg,相对标准偏差为0.29%~4.40%,加标回收率为99.64%~107.14%,可以实现高纯液氨中痕量油分的准确定量。

四川盆地东部上二叠统吴家坪组放射虫组合及其烃源意义

四川盆地东部(川东)红星地区上二叠统吴家坪组具备良好的页岩气成藏条件,是四川盆地二叠系页岩气勘探的重要接替领域,但对于该套硅泥质烃源岩的形成机理目前仍缺乏明确的认识。除了岩石矿物组成、地球化学成分、宏体化石组合等特征之外,以放射虫为代表的微体化石对于辨别硅质泥页岩沉积的成因也具有重要意义。放射虫是显生宙海洋浮游生态系统的重要组成部分,其组合可以指示沉积环境,而其丰度是古海洋生产力的良好指标。利用酸蚀法分离出放射虫化石,从而鉴别化石的形态组合特征并结合薄片镜下统计,分析了放射虫丰度与总有机碳(TOC)含量的相关性。研究结果表明,川东红星地区HY3井吴家坪组吴二段中发育大量放射虫化石,经鉴定为泡沫虫目和内射球虫目分子,属于典型的浅水放射虫组合(Se组合)。镜下统计表明吴二段放射虫丰度与TOC含量呈现较好的相关性。通过放射虫组合特征,结合吴家坪组沉积序列和古地理背景,推断吴二段烃源岩形成于水深小于60 m的较浅水环境。吴二段有机质富集可能受到初级生产力、氧化还原条件、陆源碎屑输入、上升流等多方面因素的影响,其中高古生产力是吴二段有机质富集的主控因素之一。

富有机质页岩TOC含量的地球物理定量化预测

四川盆地涪陵地区页岩气已经实现了工业化开采,而TOC含量是评价页岩有机质丰度和生烃能力的主要参数之一。利用测井资料评价烃源岩TOC含量,能够弥补烃源岩取心少、实测样品分布不连续的不足。为了得到连续的、整体的TOC含量数据,达到对页岩含气层位有机碳含量的定量化预测的目的,以涪陵地区A井为例,从目的层五峰—龙马溪组页岩实测TOC含量出发,结合地球物理测井资料,采用多参数优选、交会分析法、多元回归分析及ΔlgR法寻找与TOC相关性最好的参数,并以此建立区域性的统计学方程。利用该拟合关系式,结合三维地震数据体的叠后反演,计算出TOC数据体。结果显示,预测TOC结果与实测值高度吻合,相关性好,误差小。计算得到页岩目的层位TOC含量为0.5%-5.15%。其中目的层第一段TOC含量为1.9%-5.15%,为优势层段。地震反演得到的TOC连井分布和平面分布特征与研究区4口井的实测与计算TOC值连井对比所表现的分布特征一致。表明用岩石测井密度与TOC关系所预测的TOC分布是准确可信的,此方法可以应用于涪陵地区页岩的勘探评价。

海水中总有机碳(TOC)对牙鲆铜、铅、镉吸收的影响

该文研究了海水中Cu、Pb、Cd浓度分别为0.5mg/L时,总有机碳(TOC)浓度、种类对牙鲆(Paralichthysolivaceus)内脏、肌肉、鳃组织Cu、Pb、Cd蓄积的影响。结果表明:当海水TOC种类相同时,随TOC浓度升高,牙鲆内脏、肌肉、鳃组织Cu、Pb、Cd蓄积量均明显下降;当海水TOC浓度相同时,孔石莼分泌物比牙鲆分泌物更能降低牙鲆各组织Cu、Pb、Cd蓄积量。研究表明:孔石莼、牙鲆分泌物均能降低Cu、Pb、Cd的生物有效性;TOC浓度、种类变化对牙鲆各组织Cu、Pb、Cd蓄积分配均无影响,重金属含量大小顺序均为:内脏>鳃>肌肉。内脏团、肌肉中3种重金属蓄积量大小顺序为Cu>Pb>Cd;鳃中3种重金属蓄积量顺序为Pb>Cu>Cd。

水质TOC分析仪器的现状及其检测技术的新进展

介绍了 TOC检测的基本原理以及其相关仪器的现状 ,包括仪器的方法原理、主要类型、使用标准、仪器型号等。还详细介绍了目前可用于 TOC检测的新技术的发展状况 ,而国际上最新的有关 TOC检测的实验研究也在文章最后进行了阐述。

阳澄湖水体PI、BOD_5与TOC的相关性

采集阳澄湖中湖网围养殖区的下层水样,测定高锰酸指数(PI)、生化需氧量(BOD)5及总有机碳含量(TOC)等水质检测指标,对TOC分别与PI、BOD5的相关性进行分析;并用BOD5/TOC比值作为废水可生化性判定指标,对阳澄湖中湖的水质污染有机物状况进行综合评价。结果显示,阳澄湖水体的TOC与PI、BOD5存在显著的相关性,BOD5与PI、PI与TOC以及BOD5与TOC指标间的相关系数接近或大于0.800,但相关性存在季节变化,在7月高温季节,相关系数均高于0.900,冬季枯水期其相关系数下降至0.800及以下;阳澄湖中湖的BOD5/TOC值为0.48～0.72,平均0.59,各个季节的变化较大,非养殖季节或雨季的BOD5/TOC比值较低,暗示阳澄湖中湖的污染物成分较复杂,除了养殖活动及生活污水造成的有机污染外,工业污染、农业面源污染等占有较大的比例。

湛江特呈岛红树林区底泥TOC的释放研究

以湛江特呈岛红树林区为考察对象,通过底泥TOC向上覆海水释放的模拟对比实验,研究了红树林区底泥中TOC的释放规律和泥水比、温度、pH值三个环境因子对TOC释放的影响,同时对静态和动态体系底泥TOC的释放进行了对比。结果表明,静态下,不同条件释放规律一致,上覆海水TOC的变化呈近似"W"的趋势;泥水比增大会使底泥向上覆海水的TOC释放量增加;温度对底泥TOC释放的影响较为复杂,上覆海水温度较高或较低都有利于底泥TOC的释放;碱性条件有利于沉积物中TOC的释放;海水的运动对沉积物TOC的释放有很好的促进作用。

总有机碳分析仪测定常见水的TOC

水体有机物污染程度的综合指标有CODCr、CODMn、BOD5、TOC等,其中TOC指标是以碳的含量表示水体中有机物总量的综合指标,我国目前尚未将TOC列为常规监测项目,但是TOC作为水体重要指标将越来越受到重视。采集广州地区的常见水样:珠江水、池塘水、河涌水、污水厂进水、污水厂出水各10份用岛津公司的TOC-VCPH型总有机碳分析仪来分析,通过测定样品总碳和无机碳含量,计算二者的差值可得到总有机碳的测定值;用重铬酸钾氧化法(GB11914-89)测定了常见水样的COD。讨论了TOC和COD之间的关系,求出常见水样的COD和TOC一元回归方程,数据表明TOC和COD之间存在比较好的线性关系,可以用TOC指标代替或者计算出COD值。

基于多测井参数的陆相页岩储层总有机碳含量预测:以和尚塬地区延长组长7段为例

总有机碳含量(Total Organic Carbon,TOC)是评价页岩油气潜力的关键参数,利用测井数据预测TOC,能刻画整段储层的TOC变化,对于明确地质-工程“甜点”意义重大.和尚塬地区延长组长7段陆相页岩由于沉积环境频繁交替变化,页岩层系内大量发育粉砂质泥岩条带.针对岩性非均质性强的特点,将页岩储层细分为页岩和粉砂质泥岩两种岩性,分别建立TOC预测模型.在比较现有方法的基础上,结合研究区的测井资料实际,选用△lg R改进法和机器逐步回归学习法分岩性建立预测模型.经过实测数据的精度检验,机器逐步回归学习法的预测精度更高.通过对单井预测值与实测值的误差分析,印证了细分岩性机器逐步回归学习预测模型的可靠性,证明该方法对预测以常规测井为主的陆相泥页岩TOC是有效的.在此基础上,应用该方法对研究区101口井长7段泥页岩进行预测,获得了TOC的空间展布.

给水TOC对蒸汽氢电导率的影响

对于采用传统的离子交换除盐系统和全膜处理技术系统生产锅炉补给水的供热机组而言,其影响蒸汽氢电导率的主要因素分别是原水中有机物的残留物和还原性环境中细菌的大量繁殖。将凝胶型阴树脂换成去除有机物效果较好的D213大孔型阴树脂可显著降低传统的离子交换除盐系统出水总有机碳(total organic carbon,TOC);在超滤前后投加非氧化性杀菌剂,均可有效杀灭穿透超滤的细菌。过热蒸汽压力为12.7~18.3MPa和>18.3MPa的锅炉补给水TOC的标准值应分别控制为≤400/Nμg/L和≤200/Nμg/L(N%为供热机组的补水率,N>1)。保证过热蒸汽压力>18.3MPa的汽包炉和直流炉蒸汽质量氢电导率(25℃)标准值合格(氢电导率≤0.10μS/cm)的给水TOC标准值应为≤50μg/L。

测定水质中TOC的分析技术研究进展

水体有机物污染程度的综合指标有COD_(Cr)、COD_(Mn)、BOD_5、TOC等,其中TOC指标是以碳的含量表示水体中有机物总量的综合指标,TOC作为水体重要指标将越来越受到重视。本文对测定水质中TOC的氧化及检测方法进行了阐述和比较,总结了国内外TOC检测方法的研究现状及相关典型产品。