CN113985493B - Intelligent modeling method for underground multi-information constrained isochronous stratum grillwork - Google Patents

Intelligent modeling method for underground multi-information constrained isochronous stratum grillwork Download PDF

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CN113985493B
CN113985493B CN202111302831.0A CN202111302831A CN113985493B CN 113985493 B CN113985493 B CN 113985493B CN 202111302831 A CN202111302831 A CN 202111302831A CN 113985493 B CN113985493 B CN 113985493B
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代月
黄旭日
宋海渤
杨剑
张栋
陈小春
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Southwest Petroleum University
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Abstract

The invention provides an intelligent modeling method for an underground multi-information constrained isochronous stratum grid, which is characterized in that high-precision small-layer division results are obtained by utilizing an artificial intelligence method through comprehensive geological information and logging information, and inter-well horizons conforming to geological laws are obtained by means of seismic information based on the results, so that the small-layer point-to-surface division process is realized, and finally, the high-precision isochronous stratum grid model can be obtained. The invention can accelerate the convergence of the neural network model, refine the deposition evolution analysis of complex and other surfaces; the working efficiency can be greatly improved through an automatic procedure under the condition of more wells, and the intelligent small-layer division precision can be effectively improved through the method.

Description

地下多信息约束的等时地层格架智能建模方法Intelligent modeling method of underground multi-information constrained isochronous stratigraphic framework

技术领域Technical field

本发明属于地质数据处理技术领域,具体涉及一种地下多信息约束的等时地层格架智能建模方法。The invention belongs to the technical field of geological data processing, and specifically relates to an underground multi-information constrained isochronous stratigraphic framework intelligent modeling method.

背景技术Background technique

传统等时地层格架建立都是基于精细的地层层位,小层的层位由来主要有2种方法:The establishment of traditional isochronous stratigraphic frameworks is based on fine stratigraphic layers. There are two main methods for determining the origin of small layers:

井震结合确定大层位划分(油组界面),再基于测井和砂体信息进行细分小层,最后借助地震资料反射特征调整划分结果;比如参考文献1,霍春亮,古莉,赵春明,闫伟鹏,杨庆红.基于地震、测井和地质综合一体化的储层精细建模[J].石油学报,2007(06):66-71.提出“旋回对比,分级控制”的思路:①井震结合确定油组界面,建立“粗”的等时地层格架。②利用测井资料垂向分辨率高的特点,采用传统的等高程对比法和测井曲线相似性对比法,以砂体为单元进行井点细分层;在井震标定的基础上,以地震反演资料为背景,作联井剖面;再根据井间对比结果与地震反射特征是否一致来调整对比层位,建立精细的地层框架。The combination of well and seismic data determines the division of large layers (oil group interface), and then subdivides small layers based on well logging and sand body information. Finally, the division results are adjusted using the reflection characteristics of seismic data; for example, Reference 1, Huo Chunliang, Gu Li, Zhao Chunming , Yan Weipeng, Yang Qinghong. Reservoir fine modeling based on comprehensive integration of seismic, well logging and geology [J]. Acta Petroleum Sinica, 2007(06): 66-71. Proposed the idea of "cycle comparison, hierarchical control": ① Well Seismic combination determines the oil group interface and establishes a "coarse" isochronous stratigraphic framework. ② Taking advantage of the high vertical resolution of logging data, the traditional equal-elevation comparison method and the logging curve similarity comparison method are used to subdivide well points into layers using sand bodies as units; on the basis of well seismic calibration, The seismic inversion data is used as the background to construct well-connected profiles; then the comparison layers are adjusted based on whether the inter-well comparison results are consistent with the seismic reflection characteristics, and a fine stratigraphic framework is established.

小层的划分是基于油组层以及旋回特征信息通过插值得到。比如参考文献2,高博禹,孙立春,胡光义,张媛.基于单砂体的河流相储层地质建模方法探讨[J].中国海上油气,2008(01):34-37.,利用井点信息,应用层序地层学原理进行地层层系划分将明下段划分为4个油组;然后通过单井的时深关系(如VSP测井等)对地震资料进行井标定建立地震反射特征与测井曲线的对应关系,在地震反射剖面上解释每个具等时意义的层序界面,对应于高分辨率层序地层学这些等时界面至少应为中期基准面旋回界面;最后根据旋回叠加模式及工区内地层发育特征进行次一级的旋回划分及对比,并以4个油组的顶面趋势约束内插小层层面,以角点网格的形式处理断层与地层的关系生成构造网格模型。在构造建模过程中充分保证构造网格反映地层模式,并使同一序号的纵向网格具等时意义。The division of small layers is obtained through interpolation based on the oil group layer and cycle characteristic information. For example, reference 2, Gao Boyu, Sun Lichun, Hu Guangyi, Zhang Yuan. Discussion on geological modeling method of fluvial facies reservoir based on single sand body [J]. China Offshore Oil and Gas, 2008(01):34-37., Utilization For well point information, apply the principles of sequence stratigraphy to divide the stratigraphic strata and divide the lower section into four oil groups; then perform well calibration on the seismic data through the time-depth relationship of a single well (such as VSP logging, etc.) to establish seismic reflection characteristics. Corresponding to the well log curve, each isochronous sequence interface is interpreted on the seismic reflection profile. Corresponding to high-resolution sequence stratigraphy, these isochronic interfaces should be at least medium-term base level cycle interfaces; finally, according to the cycle The superposition model and stratigraphic development characteristics in the work area are divided into secondary cycles and compared, and the top trend constraints of the four oil groups are used to interpolate the small layer levels, and the relationship between faults and strata is processed in the form of corner grids to generate structures. Grid model. During the structural modeling process, it is fully ensured that the structural grid reflects the stratigraphic pattern, and the longitudinal grids with the same serial number have isochronous significance.

参考文献1中小层的划分方法难以确定井间层位的趋势走向,如果遇到少井目的区,井与井之间没有有效层位信息,根据这种方法难以建立精确的等时小层面(地震的纵向分辨率尺度远大于测井,所以在小层尺度上,仅仅通过地震反射特征是无法进行小层划分的);反之,如果遇到多井目的区,此方法需要对每口井进行人工层位划分,时间和人力成本较高,人为主观因素掺杂较多,影响划分精度。The method of dividing small layers in Reference 1 makes it difficult to determine the trend of layers between wells. If you encounter a target area with few wells and there is no effective layer information between wells, it is difficult to establish accurate isochronous small layers according to this method ( The longitudinal resolution scale of seismic is much larger than that of well logging, so at the small layer scale, it is impossible to divide small layers only through seismic reflection characteristics); conversely, if multiple well target areas are encountered, this method needs to conduct Manual layer division has high time and labor costs, and there are many human subjective factors, which affects the accuracy of the division.

参考文献2中是直接基于地震数据借助测井信息进行大尺度的层位划分(油层组),小层是根据内插得到,此结果明显精确度不足,由于大层与小层在地质平面的分布趋势(指深度上的起伏)由于地层岩性、地质构造等因素具有很强的非一致性,甚至会出现错误的小层划分结果,此时便需要人工修正,极大增加工作成本。In Reference 2, large-scale layer divisions (reservoir groups) are directly based on seismic data and well logging information. The small layers are obtained based on interpolation. This result is obviously insufficient in accuracy because the large layers and small layers are on the geological plane. Distribution trends (referring to fluctuations in depth) are highly inconsistent due to factors such as stratigraphic lithology and geological structure, and even erroneous layer division results may occur. At this time, manual correction is required, which greatly increases work costs.

因此传统建模过程得到的精细层位都没法很好得到小层划分结果,或者是需要大量的人力物力和时间成本才能完成,效率低下;同时,尚未有一个很好技术获得井间层位分布,即如何通过以点到面的形式把每口井上的层位转换到整个目的工区二维面中,目前的处理办法普遍为基于井资料或者地震资料划分较大尺度的砂组层位,然后通过插值以及线性等分技术建立等时地层格架模型。然而等时地层格架的精确性和地质符合度是建立在实际小层精确划分的基础之上,以上问题亟待解决。Therefore, the fine layers obtained through the traditional modeling process cannot obtain the results of small layer division very well, or it requires a lot of manpower, material resources and time costs to complete, which is inefficient; at the same time, there is not yet a good technology to obtain the interwell layers. Distribution, that is, how to convert the layers on each well into the two-dimensional plane of the entire target work area in a point-to-plane manner. The current processing method is generally to divide larger-scale sand group layers based on well data or seismic data. Then an isochronous stratigraphic framework model is established through interpolation and linear bisection techniques. However, the accuracy and geological consistency of the isochronous stratigraphic framework are based on the precise division of actual sub-layers, and the above problems need to be solved urgently.

关于智能层位划分的现有技术,文献:尚福华,李金成,原野,曹茂俊,杜睿山.基于改进BP神经网络的地层划分方法[J].计算机技术与发展,2020,30(09):148-153.作者进行地层划分其本质技术是基于测井曲线利用神经网络进行岩性分类。选取最基础的三层BP神经网络,以岩性类别作为地层划分的依据。岩性的突变的采样点为小层的分层点。因此,岩性的识别是工作的重点。同时,作者选取了L-M算法进行改进优化神经网络训练效果;作者选的取自然电位曲线、自然伽马曲线作为岩性划分的主要曲线,再参考电阻率、声波时差、密度、中子曲线特征,进行小层划分。作者选取的是改进的BP神经网络,这类网络是目前发展较为成熟的一类基础神经网络,但存在很多不足:1.当处理一些复杂的非线性化问题时,网络的训练容易陷入局部极小值,从而导致训练失败,得不到一个收敛的神经网络模型;2.收敛速度慢,即算法的效率很低;3.神经网络的结构选择没有一套系统的理论指导,都是专家经验选择,因此对于不同目标工区、不同地质条件、不同的层位划分来说,其泛化能力存在不足;4.此网络对样本依赖性较大,对数据质量要求很高。Regarding the existing technology of intelligent horizon division, literature: Shang Fuhua, Li Jincheng, Yuan Ye, Cao Maojun, Du Ruishan. Stratum division method based on improved BP neural network [J]. Computer Technology and Development, 2020, 30(09): 148-153. The author’s essential technology for stratigraphic division is to use neural networks to classify lithology based on well logging curves. The most basic three-layer BP neural network is selected, and the lithology category is used as the basis for stratigraphic division. The sampling points for sudden changes in lithology are the stratification points of small layers. Therefore, the identification of lithology is the focus of the work. At the same time, the author chose the L-M algorithm to improve and optimize the neural network training effect; the author chose the natural potential curve and the natural gamma curve as the main curves for lithology classification, and then referred to the resistivity, acoustic time difference, density, and neutron curve characteristics, Carry out small layer division. The author chose an improved BP neural network. This type of network is a relatively mature basic neural network at present, but it has many shortcomings: 1. When dealing with some complex nonlinear problems, the training of the network can easily fall into local extremes. Small value, which leads to training failure and a converged neural network model cannot be obtained; 2. The convergence speed is slow, that is, the efficiency of the algorithm is very low; 3. The structure selection of the neural network does not have a systematic theoretical guidance and is all based on expert experience. Therefore, for different target work areas, different geological conditions, and different stratigraphic divisions, its generalization ability is insufficient; 4. This network is highly dependent on samples and has high requirements for data quality.

文献,刘英杰.智能化地层对比技术方法及应用[D].燕山大学,2013.利用高斯模型通过可信度计算以及控制分层界面的选择,得到较为准确的分层结果;岩性层段确定之后,根据测井属性特征,提取各层段的自然伽马、自然电位、声波时差、砂层厚度以及韵律等参数,并以此为各小层的特征属性,采用概率神经网络(PNN)进行井间地层对比,并连线。此方法的数据预处理过程较为复杂:通过测井属性特征归纳总结测井曲线形韵律类型及特征向量表,即把图像型的参数转化为数值型让计算机能够识别,此外还对自然伽马曲线(GR)、声波时差曲线(AC)、自然电位曲线(SP)的滤波处理,将信号进行过滤,从而实现将测井数据方波化。其学习样本的建立参考了一口标准井,而神经网络性能的验证仅选取了4口井,对于油田工区而言,无法验证其适用性,且数据集过少的使用是不能够广泛且精确地通过层位划分为后续建模工作提供有效、全面的地质信息。Literature, Liu Yingjie. Intelligent stratigraphic correlation technology methods and applications [D]. Yanshan University, 2013. Using the Gaussian model to obtain more accurate stratification results through credibility calculation and control the selection of stratification interfaces; determination of lithological intervals After that, according to the characteristics of the well logging attributes, the parameters such as natural gamma, natural potential, acoustic time difference, sand layer thickness and rhythm of each layer are extracted, and these are used as the characteristic attributes of each small layer, using the Probabilistic Neural Network (PNN). Compare the stratigraphy between wells and connect them. The data preprocessing process of this method is relatively complex: the logging curve rhythm types and feature vector tables are summarized through the logging attribute characteristics, that is, the image type parameters are converted into numerical types so that the computer can recognize them. In addition, the natural gamma curve is also processed (GR), acoustic transit time curve (AC), and natural potential curve (SP) are filtered to filter the signals, thereby converting the logging data into square waves. The establishment of the learning sample refers to a standard well, and the verification of the neural network performance only selects 4 wells. For the oil field work area, its applicability cannot be verified, and the use of too few data sets cannot be widely and accurately used. Provide effective and comprehensive geological information for subsequent modeling work through layer division.

因此,现有的智能层位划分方法一般只针对于单井信息,在划分层位的同时尚未考虑目标井周围的地质信息以及地震信息,最多是用这两类信息在得到初步划分结果后进行约束修正,且人工智能应用于地球物理解释领域都存在一些共同的挑战:数据集大小、数据质量、模型收敛速度、最后的应用效果优劣等。Therefore, the existing intelligent layer division methods generally only focus on single well information. When dividing layers, the geological information and seismic information around the target well are not considered. At most, these two types of information are used after the preliminary division results are obtained. There are some common challenges in constraint correction and the application of artificial intelligence in the field of geophysical interpretation: data set size, data quality, model convergence speed, final application effect, etc.

发明内容Contents of the invention

针对上述技术问题,本发明提供一种地下多信息约束的等时地层格架智能建模方法,以测井和地质信息为主,在基于测井曲线进行小层智能划分的同时通过数据集制作方式加入地质约束,让地质信息参与整个划分过程,进而得到较为符合地质信息的结果。以精细的小层划分为基础,借助地震反演信息和神经网络,解决井间层位划分的问题,建立高精度的等时地层格架模型。整个方法通过人工智能技术能够有效提高工序效率,且含有地质约束数据集的制作能够打破神经网络对数据集大小、模型收敛速度等方面的限制。In view of the above technical problems, the present invention provides an underground multi-information constrained isochronous stratigraphic framework intelligent modeling method, which mainly focuses on well logging and geological information. It performs intelligent division of small layers based on well logging curves and simultaneously creates through data sets. This method adds geological constraints and allows geological information to participate in the entire division process, thereby obtaining results that are more consistent with geological information. Based on the fine layer division, with the help of seismic inversion information and neural network, we solve the problem of layer division between wells and establish a high-precision isochronous stratigraphic framework model. The entire method can effectively improve process efficiency through artificial intelligence technology, and the production of geologically constrained data sets can break the limitations of neural networks on data set size and model convergence speed.

具体技术方案为:The specific technical solutions are:

地下多信息约束的等时地层格架智能建模方法,其特征在于,包括以下步骤:The underground multi-information constrained isochronous stratigraphic framework intelligent modeling method is characterized by including the following steps:

S1.根据专家经验对目的工区进行分析,选取岩性敏感的测井曲线,确定作为神经网络数据集的曲线种类为C并且每条曲线样本点数为L,根据已有测井信息数量确定作为神经网络训练集的样本N口井,确定预测集样本即需要划分小层的M口盲井;S1. Analyze the target work area based on expert experience, select lithology-sensitive logging curves, determine the curve type as the neural network data set as C and the number of sample points for each curve as L, and determine the neural network data set as the neural network data set based on the amount of existing logging information. There are N wells as samples in the network training set, and it is determined that the prediction set samples are M blind wells that need to be divided into small layers;

S2.对工区进行地质分析,确定物源方向α和垂直物源方向β,β-α=90°,基于α和β将整个工区测井划分为编号为①-④的4部分;S2. Conduct geological analysis on the work area, determine the source direction α and vertical source direction β, β-α = 90 ° , and divide the entire work area logging into 4 parts numbered ①-④ based on α and β;

S3.根据上述划分工区,进行神经网络训练数据集制作;S3. According to the above-mentioned divided work areas, prepare the neural network training data set;

制作流程为:确定一个连井样本由Q口井组成,每口井有C条测井曲线已经确定,按照顺物源和垂直物源方向分别制作两套训练集样本数的25%大小数据,其余50%数据样本通过随机组合的方式进行;The production process is as follows: determine that a connected well sample consists of Q wells, and C logging curves for each well have been determined. Two sets of 25% size data of the training set samples are produced according to the direction of the source and the direction perpendicular to the source. The remaining 50% of the data samples are randomly combined;

S3.1、顺物源方向:对①③区域的井训练样本进行编号Ni,i=1,2,3…,N1,样本数为N1;对②④区域的井训练样本进行编号Ni,i=1,2,3…,N2,样本数为N2;其中N=N1+N2,根据式(1)生成组成一个样本的井序号Wk,k=1,2…,Q;S3.1. Along the source direction: number the well training samples in the ①③ area N i , i=1,2,3...,N 1 , and the number of samples is N 1 ; number the well training samples in the ②④ area N i , i=1,2,3…,N 2 , the number of samples is N 2 ; where N=N 1 +N 2 , the well number W k that constitutes a sample is generated according to formula (1), k=1,2…, Q;

Ia=[Nj*random(0~1)+0.5] (1)I a =[N j *random(0~1)+0.5] (1)

[]表示向上取整,j=1,2,当j=1,取①③区域的井样本进行随机组合,此时Nj=N1,当j=2,取②④区域样本进行随机组合,此时Nj=N2[] means rounding up, j=1,2. When j=1, take the well samples from the ①③ area for random combination. At this time, N j =N 1 . When j=2, take the well samples from the ②④ area for random combination. This When N j =N 2 ;

S3.2、垂直物源方向:对①②区域的井训练样本进行编号Ni,i=1,2,3…,N3,样本数为N3;对③④区域的井训练样本进行编号Ni,i=1,2,3…,N4,样本数为N4;其中N=N3+N4,根据式(2)生成组成一个样本的井序号Wk,k=1,2..,Q;S3.2. Vertical source direction: number the well training samples in the ①② area N i , i=1,2,3...,N 3 , and the number of samples is N 3 ; number the well training samples in the ③④ area N i , i=1,2,3...,N 4 , the number of samples is N 4 ; where N=N 3 +N 4 , the well number W k that constitutes a sample is generated according to formula (2), k=1,2.. ,Q;

Iv=[Nj*random(0~1)+0.5] (2)I v =[N j *random(0~1)+0.5] (2)

[]表示向上取整,j=3,4,当j=3,取①②区域的井样本进行随机组合,此时Nj=N3;当j=4,取③④区域样本进行随机组合,此时Nj=N4[] means rounding up, j=3,4. When j=3, take the well samples from the ①② area for random combination. At this time, N j =N 3 ; when j=4, take the well samples from the ③④ area for random combination. This When N j =N 4 ;

S3.3、随机组合:对工区中所有训练样本统一编号Ni,i=1,2,3…,N;根据式(3)生成组成一个样本的井序号Wk,k=1,2..,Q;S3.3. Random combination: All training samples in the work area are uniformly numbered N i , i=1,2,3...,N; according to formula (3), the well sequence number W k that constitutes a sample is generated, k=1,2. .,Q;

Ir=[N*random(0~1)+0.5] (3)I r =[N*random(0~1)+0.5] (3)

[]表示向上取整,N为训练样本总数;[] means rounding up, N is the total number of training samples;

根据以上三种方式组成神经网络训练集。The neural network training set is composed according to the above three methods.

S4.对预测样本编号Mj,j=1,2,3…M,对所有训练样本编号Ni,i=1,2,3…,N,根据式(4)生成井序号Wk与Mj组成一个样本,k=1,2..,Q-1;也就是训练集取Q-1口井与预测集中的1口一共组成一个预测样本;S4. Number the prediction samples M j , j = 1, 2, 3...M, and number all the training samples Ni , i = 1, 2, 3..., N. Generate well numbers W k and M according to Equation (4) j forms a sample, k=1,2..,Q-1; that is, Q-1 wells in the training set and 1 well in the prediction set form a prediction sample;

Ip=[N*random(0~1)+0.5] (4)I p =[N*random(0~1)+0.5] (4)

[]表示向上取整,N为训练样本数;[] means rounding up, N is the number of training samples;

最后,根据S3中获得的训练集与生成的预测集组成最终的神经网络数据集;Finally, the final neural network data set is formed based on the training set obtained in S3 and the generated prediction set;

S5.构建特征金字塔网络FPN,利用S4中的训练集进行模型训练;S5. Construct the feature pyramid network FPN and use the training set in S4 for model training;

S6.利用S5中得到的模型对目的工区中的预测集即盲井进行批量的小层划分;S6. Use the model obtained in S5 to divide the prediction set in the target work area, that is, the blind well into batches of small layers;

S7.在目的工区中将S6得到的层位划分结果作为边界,基于地震资料进行目的井段的随机优化反演得到波阻抗反演初始模型,将模型中每个样点作为伪井,选取伪井按照S3中随机组合方法制作新样本集进行训练,基于收敛模型并预测其余未知层位信息的伪井,进而得到整个工区的小层二维层面,再根据真实测井的人工分层对此层位面进行误差计算,在井点误差计算基础上进行插值得到校正结果;S7. In the target work area, use the layer division result obtained in S6 as the boundary, perform random optimization inversion of the target well section based on seismic data to obtain the initial wave impedance inversion model, use each sample point in the model as a pseudo well, and select the pseudo well. The wells are trained according to the random combination method in S3 to create a new sample set. Based on the convergence model, the remaining unknown layer information is predicted for the pseudo wells, and then the small two-dimensional layers of the entire work area are obtained, and then the artificial stratification of the real well logs is used. Error calculation is performed on the layer plane, and interpolation is performed on the basis of well point error calculation to obtain the correction result;

S8.重复步骤S7,以井层位为约束条件不断对智能地层划分结果进行优化校正得到最终符合地质规律的二维层位划分结果;S8. Repeat step S7, and continuously optimize and correct the intelligent stratigraphic division results with the well layer as the constraint condition to obtain the final two-dimensional stratigraphic division result that conforms to geological laws;

S9.基于S8得到的结果作为层位约束得到最后的等时地层格架模型。S9. Based on the results obtained in S8 as horizon constraints, the final isochronous stratigraphic framework model is obtained.

本发明技术方案带来的有益效果:The beneficial effects brought by the technical solution of the present invention are:

1.步骤S2的数据集制作方法的有益效果:能够加速神经网络模型收敛;同时此方法抛弃了传统单井信息特征提取的神经网络学习方式,通过连井数据的形式将一维测井数据转变为二维数据提高了FPN网络的学习效率、能更好地保存井与井之间的地质空间特征,同时也将地质信息加入数据集作为约束条件,并贯穿整个智能小层划分过程,有利最后得到更加符合真实地质信息的层位划分结果;在井数较少情况下,还能通过连井数据集的制作进行数据增广解决数据缺少无法训练神经网络模型的问题,在井数较多的情况下通过自动化工序能够极大提升工作效率。1. The beneficial effects of the data set creation method in step S2: it can accelerate the convergence of the neural network model; at the same time, this method abandons the traditional neural network learning method of single well information feature extraction, and transforms one-dimensional well logging data in the form of connected well data. It improves the learning efficiency of the FPN network for two-dimensional data and can better preserve the geological spatial characteristics between wells. It also adds geological information to the data set as a constraint and runs through the entire intelligent layer division process, which is beneficial to the final Obtain layer division results that are more in line with real geological information; when the number of wells is small, data augmentation can also be performed through the production of well-connected data sets to solve the problem of lack of data and inability to train neural network models. When the number of wells is large, In this case, work efficiency can be greatly improved through automated processes.

2.选取FPN网络进行小层划分任务,能够同时做到识别小层种类和精确检测分层线位置,其网络结构和多尺度检测的功能不仅能保留层位在地质空间中更多的连通信息还能提升模型的运行效率和对小尺度目标的检测性能,相较于选择普通的神经网络实现单纯层位分类任务,此方法能够有效提高智能小层划分精度。2. Select the FPN network for the task of dividing small layers, which can simultaneously identify the types of small layers and accurately detect the position of layering lines. Its network structure and multi-scale detection function can not only retain more connected information of layers in geological space It can also improve the operating efficiency of the model and the detection performance of small-scale targets. Compared with choosing an ordinary neural network to achieve simple layer classification tasks, this method can effectively improve the accuracy of intelligent small layer division.

3.综合地质信息和测井信息利用人工智能方法得到高精度的小层划分结果,再基于此结果借助地震信息获得符合地质规律的井间层位,由此实现小层由点到面的划分过程,最终能够得到高精度的等时地层格架模型。3. Integrate geological information and well logging information and use artificial intelligence methods to obtain high-precision sub-layer division results. Based on this result, seismic information is used to obtain inter-well layers that conform to geological laws, thereby achieving point-to-surface division of sub-layers. process, a high-precision isochronous stratigraphic framework model can finally be obtained.

附图说明Description of drawings

图1为实施例的工区划分结果;Figure 1 shows the work area division results of the embodiment;

图2为实施例的一个连井样本结构;Figure 2 is a well-connected sample structure according to the embodiment;

图3为实施例的盲井智能小层划分结果;Figure 3 shows the intelligent sub-layer division results of the blind well according to the embodiment;

图4为实施例的波阻抗反演初始模型;Figure 4 is the initial model of wave impedance inversion according to the embodiment;

图5为实施例的层位校正。Figure 5 shows the layer correction according to the embodiment.

具体实施方式Detailed ways

结合实施例说明本发明的具体技术方案。The specific technical solutions of the present invention will be described with reference to examples.

地下多信息约束的等时地层格架智能建模方法:Intelligent modeling method of underground multi-information constrained isochronous stratigraphic framework:

S1.根据专家经验对目的工区进行分析,选取对砂泥岩等岩性敏感的测井曲线,确定作为神经网络数据集的曲线种类为C并且每条曲线样本点数为L,根据已有测井信息数量确定作为神经网络训练集的样本N口井,确定预测集样本即需要划分小层的M口盲井;S1. Analyze the target work area based on expert experience, select well logging curves that are sensitive to lithology such as sand and mudstone, determine the type of curve as the neural network data set as C and the number of sample points for each curve as L, according to the existing well logging information Determine the number of sample N wells as the neural network training set, and determine the prediction set samples, that is, M blind wells that need to be divided into small layers;

S2.对工区进行地质分析,确定物源方向α和垂直物源方向β,β-α=90°,基于α和β将整个工区测井划分为编号为①-④的4部分,如图1,实线为切割分界线,箭头所指方向为物源方向;S2. Conduct geological analysis on the work area, determine the source direction α and vertical source direction β, β-α = 90 ° , and divide the entire work area logging into 4 parts numbered ①-④ based on α and β, as shown in Figure 1 , the solid line is the cutting dividing line, and the direction pointed by the arrow is the source direction;

S3.根据上述划分工区,进行神经网络训练数据集制作,其制作流程为:确定一个连井样本由Q口井组成,每口井有C条测井曲线(在步骤S1)已经确定,则一个样本为图2所示,按照顺物源和垂直物源方向分别制作两套训练集样本数的25%大小数据,其余50%数据样本通过随机组合的方式进行。S3. According to the above-mentioned division of work areas, the neural network training data set is produced. The production process is as follows: determine that a well-connected sample consists of Q wells, and each well has C logging curves (in step S1). It has been determined, then a The samples are shown in Figure 2. Two sets of 25% of the training set samples were produced according to the direction of the source and perpendicular to the source, and the remaining 50% of the data samples were randomly combined.

S3.1、顺物源方向:对①③区域的井训练样本(样本数为N1)进行编号Ni(i=1,2,3…,N1),对②④区域的井训练样本(样本数为N2)进行编号Ni(i=1,2,3…,N2),其中N=N1+N2,根据式(1)生成组成一个样本的井序号Wk(k=1,2…,Q);S3.1. Along the source direction: number N i (i=1,2,3...,N 1 ) for the well training samples (number of samples is N 1 ) in the ①③ area, and number the well training samples (samples) in the ②④ area The number is N 2 ) and numbered Ni ( i =1,2,3...,N 2 ), where N=N 1 +N 2 , and the well number W k (k=1) that constitutes a sample is generated according to the formula (1) ,2…,Q);

Ia=[Nj*random(0~1)+0.5] (1)I a =[N j *random(0~1)+0.5] (1)

[]表示向上取整,j=1,2,当j=1,取①③区域的井样本进行随机组合,此时Nj=N1,当j=2,取②④区域样本进行随机组合,此时Nj=N2[] means rounding up, j=1,2. When j=1, take the well samples from the ①③ area for random combination. At this time, N j =N 1 . When j=2, take the well samples from the ②④ area for random combination. This When N j =N 2 ;

S3.2、垂直物源方向:对①②区域的井训练样本(样本数为N3)进行编号Ni(i=1,2,3…,N3),对③④区域的井训练样本(样本数为N4)进行编号Ni(i=1,2,3…,N4),其中N=N3+N4,根据式(2)生成组成一个样本的井序号Wk(k=1,2..,Q);S3.2. Vertical source direction: number N i (i=1,2,3...,N 3 ) for the well training samples (number of samples is N 3 ) in areas ①②, and number the training samples (samples) of wells (samples) in areas ③④ The number is N 4 ) and numbered Ni ( i =1,2,3...,N 4 ), where N=N 3 +N 4 , and the well sequence number W k (k=1) that constitutes a sample is generated according to the formula (2) ,2..,Q);

Iv=[Nj*random(0~1)+0.5] (2)I v =[N j *random(0~1)+0.5] (2)

[]表示向上取整,j=3,4,当j=3,取①②区域的井样本进行随机组合,此时Nj=N3,当j=4,取③④区域样本进行随机组合,此时Nj=N4[] means rounding up, j=3,4. When j=3, take the well samples from the ①② area for random combination. At this time, N j =N 3. When j=4, take the well samples from the ③④ area for random combination. This When N j =N 4 ;

S3.3、随机组合:对工区中所有训练样本统一编号Ni(i=1,2,3…,N)根据式(3)生成组成一个样本的井序号Wk(k=1,2..,Q);S3.3. Random combination: Unify number N i (i=1,2,3...,N) for all training samples in the work area and generate the well sequence number W k (k=1,2) that constitutes a sample according to equation (3). .,Q);

Ir=[N*random(0~1)+0.5] (3)I r =[N*random(0~1)+0.5] (3)

[]表示向上取整,N为训练样本总数;[] means rounding up, N is the total number of training samples;

根据以上三种方式组成神经网络训练集。The neural network training set is composed according to the above three methods.

S4.对预测样本编号Mj(j=1,2,3…M),对所有训练样本编号Ni(i=1,2,3…,N),根据式(4)生成井序号Wk(k=1,2..,Q-1)与Mj组成一个样本(也就是训练集取Q-1口井与预测集中的1口一共组成一个预测样本;S4. Number the prediction samples M j (j=1,2,3...M), number all the training samples N i (i=1,2,3...,N), and generate the well sequence number W k according to Equation (4) (k=1,2..,Q-1) and M j form a sample (that is, Q-1 wells in the training set and 1 well in the prediction set form a prediction sample;

Ip=[N*random(0~1)+0.5] (4)I p =[N*random(0~1)+0.5] (4)

[]表示向上取整,N为训练样本数;[] means rounding up, N is the number of training samples;

最后,根据S3中获得的训练集与生成的预测集组成最终的神经网络数据集;Finally, the final neural network data set is formed based on the training set obtained in S3 and the generated prediction set;

S5.构建特征金字塔网络(FPN),利用S4中的训练集进行模型训练;S5. Construct a feature pyramid network (FPN) and use the training set in S4 for model training;

S6.利用S5中得到的模型对目的工区中的预测集即盲井进行批量的小层划分,如图3;S6. Use the model obtained in S5 to divide the prediction set in the target work area, that is, the blind well into batches of small layers, as shown in Figure 3;

S7.在目的工区中将S6得到的层位划分结果作为边界,基于地震资料进行目的井段的随机优化反演得到波阻抗反演初始模型,如图4,将模型中每个样点作为伪井,选取伪井按照S3中随机组合方法制作新样本集进行训练,基于收敛模型并预测其余未知层位信息的伪井,进而得到整个工区的小层二维层面,再根据真实测井的人工分层对此层位面进行误差计算,在井点误差计算基础上进行插值得到校正结果,如图5;S7. In the target work area, use the layer division result obtained in S6 as the boundary, and conduct stochastic optimization inversion of the target well section based on seismic data to obtain the initial wave impedance inversion model, as shown in Figure 4. Each sample point in the model is used as a pseudo Wells, select pseudo wells to create a new sample set for training according to the random combination method in S3, and predict the remaining pseudo wells with unknown layer information based on the convergence model, thereby obtaining the small two-dimensional layers of the entire work area, and then based on the artificial data of the real well logging Calculate the error of this layer layer by layer, and perform interpolation based on the well point error calculation to obtain the correction result, as shown in Figure 5;

S8.重复步骤S7,以井层位为约束条件不断对智能地层划分结果进行优化校正得到最终符合地质规律的二维层位划分结果;S8. Repeat step S7, and continuously optimize and correct the intelligent stratigraphic division results with the well layer as the constraint condition to obtain the final two-dimensional stratigraphic division result that conforms to geological laws;

S9.基于S8得到的结果作为层位约束得到最后的等时地层格架模型。S9. Based on the results obtained in S8 as horizon constraints, the final isochronous stratigraphic framework model is obtained.

Claims (3)

1. The intelligent modeling method for the underground multi-information constrained isochronous stratum grillage is characterized by comprising the following steps of:
s1, analyzing a target work area according to expert experience, selecting lithology-sensitive logging curves, determining that the type of the curves serving as a neural network data set is C, the number of sample points of each curve is L, determining N wells serving as a neural network training set according to the number of existing logging information, and determining a prediction set sample, namely M blind wells needing to be divided into small layers;
s2, carrying out geological analysis on the work area, determining an object source direction alpha and a vertical object source direction beta, wherein beta-alpha=90 DEG, and dividing the whole work area well logging into 4 parts numbered (1) - (4) based on alpha and beta;
s3, manufacturing a neural network training data set according to the divided work areas;
s4, numbering predicted samples, and numbering all training samples to generate a sample predicted sample; forming a final neural network data set according to the training set obtained in the step S3 and the generated prediction set;
s5, constructing a feature pyramid network FPN, and performing model training by using the training set in the S4;
s6, carrying out batch small-layer division on a prediction set, namely blind wells, in the target work area by using the model obtained in the S5;
s7, taking the horizon dividing result obtained in the S6 as a boundary in a target work area, carrying out random optimization inversion on a target well section based on seismic data to obtain a wave impedance inversion initial model, taking each sample point in the model as a pseudo well, selecting the pseudo well to manufacture a new sample set for training according to a random combination method in the S3, and predicting pseudo wells with other unknown horizon information based on a convergence model to further obtain a small two-dimensional layer of the whole work area, carrying out error calculation on the horizon according to manual layering of a real logging, and carrying out interpolation on the basis of well point error calculation to obtain a correction result;
s8, repeating the step S7, and continuously optimizing and correcting the intelligent stratum division result by taking the well horizon as a constraint condition to obtain a two-dimensional horizon division result which finally accords with a geological rule;
s9, obtaining a final isochronous stratigraphic grid model based on the result obtained in the S8 as horizon constraint.
2. The intelligent modeling method for the underground multi-information constrained isochronous stratigraphic framework according to claim 1, wherein the neural network training data set is created in the step S3;
the manufacturing flow is as follows: determining that one well connecting sample consists of Q wells, wherein each well has C well logging curves, respectively manufacturing 25% of the number of two sets of training set samples according to the direction of a material source and a vertical material source, and carrying out the rest 50% of data samples in a random combination mode;
s3.1, following the object source direction: numbering N the well training samples of the (1) (3) region i ,i=1,2,3…,N 1 The number of samples is N 1 The method comprises the steps of carrying out a first treatment on the surface of the Numbering N the well training samples of the (2) (4) region i ,i=1,2,3…,N 2 The number of samples is N 2 The method comprises the steps of carrying out a first treatment on the surface of the Where n=n 1 +N 2 Generating a well sequence number W constituting a sample according to formula (1) k ,k=1,2…,Q;
I a =[N j *random(0~1)+0.5] (1)
[]Representing a round-up, j=1, 2, where N is the random combination of the well samples taken from the (1) (3) region when j=1 j =N 1 When j=2, taking (2) (4) area samples for random combination, N j =N 2
S3.2, vertical object source direction: numbering N the well training samples of the (1) (2) regions i ,i=1,2,3…,N 3 The number of samples is N 3 The method comprises the steps of carrying out a first treatment on the surface of the Numbering N the well training samples of the (3) (4) region i ,i=1,2,3…,N 4 The number of samples is N 4 The method comprises the steps of carrying out a first treatment on the surface of the Where n=n 3 +N 4 Generating a well sequence number W constituting a sample according to formula (2) k ,k=1,2..,Q;
I v =[N j *random(0~1)+0.5] (2)
[]Representing a round-up, j=3, 4, where N is the random combination of the well samples taken from the (1) (2) region when j=3 j =N 3 The method comprises the steps of carrying out a first treatment on the surface of the When j=4, taking (3) (4) area samples for random combination, N j =N 4
S3.3, random combination: uniformly numbering N for all training samples in work area i I=1, 2,3 …, N; generating a well sequence number W constituting a sample according to formula (3) k ,k=1,2..,Q;
I r =[N*random(0~1)+0.5] (3)
[] Representing the upward rounding, wherein N is the total number of training samples;
and forming a neural network training set according to the three modes.
3. The method for intelligent modeling of an underground multi-information constrained isochronous stratigraphic framework according to claim 1, wherein S4 specifically comprises: numbering predicted samples M j J=1, 2,3 … M, number N for all training samples i I=1, 2,3 …, N, the well number W is generated according to equation (4) k And M is as follows j One sample was composed, k=1, 2., Q-1; namely, the training set takes a predicted sample which is formed by 1 well in the prediction set and 1 well in the Q-1 well;
I p =[N*random(0~1)+0.5] (4)
[] Represents the rounding up, N is the number of training samples.
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