技术领域Technical Field
本发明涉及石油与天然气钻井安全监测技术领域，具体涉及一种水平井环空岩屑浓度预测方法及装置、存储介质。The invention relates to the technical field of oil and gas drilling safety monitoring, and in particular to a method and device for predicting cuttings concentration in annular space of a horizontal well, and a storage medium.
背景技术Background Art
环空岩屑浓度是指在环空中钻井液所含岩屑的浓度，通常以百分数表示，是影响钻井作业的重要参数。在水平井钻进过程中，环空岩屑浓度是监测岩屑运移情况、分析井眼清洁效果的重要评价指标之一。井眼清洁不良问题可导致高摩高扭、长时间循环、频繁划眼，严重时会导致卡钻、填井侧钻等复杂事故。因此，采用合理钻井参数和施工工序促进地层破碎岩屑及时返出井筒，使环空岩屑浓度处于合理范围，对于提高钻井速度，缩短钻井周期，保障钻井安全具有重要意义。Annular cuttings concentration refers to the concentration of cuttings contained in the drilling fluid in the annulus, usually expressed as a percentage, and is an important parameter that affects drilling operations. During horizontal well drilling, the annular cuttings concentration is one of the important evaluation indicators for monitoring the migration of cuttings and analyzing the effect of wellbore cleaning. Poor wellbore cleaning can lead to high friction and torque, long circulation, and frequent reaming. In severe cases, it can lead to complex accidents such as stuck drill, well filling and sidetracking. Therefore, the use of reasonable drilling parameters and construction procedures to promote the timely return of formation broken cuttings to the wellbore and keep the annular cuttings concentration within a reasonable range is of great significance for increasing drilling speed, shortening drilling cycle, and ensuring drilling safety.
现有的环空岩屑浓度预测方法中主要采用机器学习和硬件测量方法，其中机器学习方法对于岩屑浓度影响因素采用经验方法确定，定性判断影响了智能分析的科学性，并且机器学习方法只适合直井，不能应用到水平井中。硬件测量方法依赖于硬件设备，由于质量流量计价格昂贵，无法实现全场景覆盖，因此限制了该方法的应用。The existing annular cuttings concentration prediction methods mainly use machine learning and hardware measurement methods. The machine learning method uses empirical methods to determine the factors affecting cuttings concentration. Qualitative judgment affects the scientific nature of intelligent analysis. In addition, the machine learning method is only suitable for vertical wells and cannot be applied to horizontal wells. The hardware measurement method relies on hardware equipment. Since mass flow meters are expensive and cannot achieve full scene coverage, the application of this method is limited.
发明内容Summary of the invention
本发明提供一种水平井环空岩屑浓度预测方法及装置、存储介质，以提高环空岩屑浓度预测的准确性和适应性。The present invention provides a method and device for predicting the concentration of cuttings in the annulus of a horizontal well, and a storage medium, so as to improve the accuracy and adaptability of the prediction of the concentration of cuttings in the annulus of the horizontal well.
为此，本发明提供如下技术方案：To this end, the present invention provides the following technical solutions:
一种水平井环空岩屑浓度预测方法，所述方法包括：A method for predicting cuttings concentration in annulus of a horizontal well, the method comprising:
按井斜角对水平井进行分段；Segment the horizontal well according to the well inclination;
根据原始录井数据确定水平井各段的岩屑浓度影响参数；Determine the cuttings concentration influencing parameters of each section of the horizontal well based on the original logging data;
建立各段对应的岩屑浓度影响参数与岩屑浓度的非线性预测函数；Establish the nonlinear prediction function of the cuttings concentration influencing parameters and cuttings concentration corresponding to each section;
利用所述预测函数对水平井环空岩屑浓度进行预测。The prediction function is used to predict the cuttings concentration in the annulus of the horizontal well.
可选地，所述按井斜角对水平井进行分段包括：按井斜角0-30°、30-65°和65+°对水平井进行分段。Optionally, segmenting the horizontal well according to the well inclination angle includes: segmenting the horizontal well according to the well inclination angles of 0-30°, 30-65° and 65+°.
可选地，所述根据原始录井数据确定水平井各段的岩屑浓度影响参数包括：Optionally, determining the cuttings concentration influencing parameters of each section of the horizontal well according to the original logging data includes:
获取水平井各段的原始录井数据；Obtain the original logging data of each section of the horizontal well;
根据所述原始录井数据生成维标准化样本矩阵，；Generated according to the original logging data dimensional standardized sample matrix, ;
确定所述维标准化样本矩阵的相关系数矩阵；Determine the The correlation coefficient matrix of the dimensionally standardized sample matrix;
根据所述相关系数矩阵得到个特征向量；According to the correlation coefficient matrix, we can get feature vectors;
利用所述个特征向量和所述相关系数矩阵，确定多个主成分作为岩屑浓度影响参数。Using the The eigenvectors and the correlation coefficient matrix are used to determine multiple principal components as cuttings concentration influencing parameters.
可选地，所述根据所述原始录井数据生成维标准化样本矩阵包括：Optionally, the generating according to the original logging data The dimensionally standardized sample matrix includes:
根据所述原始录井数据生成维样本矩阵；Generated according to the original logging data dimensional sample matrix;
对所述维样本矩阵中的阵元进行标准化变换，得到维标准化样本矩阵。Regarding the The array elements in the dimensional sample matrix are normalized and transformed to obtain dimensional standardized sample matrix.
可选地，所述根据所述相关系数矩阵得到个特征向量包括：Optionally, the correlation coefficient matrix is obtained The feature vectors include:
根据所述相关系数矩阵构建正交矩阵；Constructing an orthogonal matrix according to the correlation coefficient matrix;
将所述正交矩阵的每一列作为对应的特值的特征向量，得到个特征向量。Taking each column of the orthogonal matrix as the eigenvector of the corresponding special value, we get feature vectors.
可选地，所述利用所述个特征向量和所述相关系数矩阵，确定多个主成分作为岩屑浓度影响参数包括：Optionally, the use of The characteristic vectors and the correlation coefficient matrix determine multiple principal components as cuttings concentration influencing parameters including:
根据所述个特征向量和所述相关系数矩阵求解特征方程，得到个主成分；According to the The characteristic vectors and the correlation coefficient matrix are used to solve the characteristic equation and we get principal components;
根据个主成分的贡献率从中选出多个主成分，将选出的主成分中的参数作为岩屑浓度影响参数。according to The contribution rates of the principal components are calculated to select multiple principal components, and the parameters in the selected principal components are used as the parameters affecting the cuttings concentration.
可选地，建立岩屑浓度影响参数与岩屑浓度的非线性预测函数包括：Optionally, establishing a nonlinear prediction function of the cuttings concentration influencing parameter and the cuttings concentration includes:
获取对应所述岩屑浓度影响参数的样本数据；Acquiring sample data corresponding to the cuttings concentration influencing parameter;
对所述样本数据进行归一化处理，得到处理后的样本数据；Performing normalization processing on the sample data to obtain processed sample data;
利用所述处理后的样本数据训练BP神经网络模型，所述BP神经网络的输入节点为所述多个主成分对应的特征向量，输出为岩屑浓度预测结果。The processed sample data is used to train a BP neural network model, the input nodes of the BP neural network are the characteristic vectors corresponding to the multiple principal components, and the output is the prediction result of the cuttings concentration.
可选地，所述BP神经网络隐含层各神经元的输入函数和输出函数分别为：Optionally, the input function and output function of each neuron in the hidden layer of the BP neural network are respectively:
； ;
； ;
其中，为输入函数，为输出函数；为输入参数，为权重，为偏置值，表示的分布函数。in, is the input function, is the output function; is the input parameter, is the weight, is the bias value, express The distribution function of .
一种水平井环空岩屑浓度预测装置，所述装置包括：A device for predicting cuttings concentration in annular space of a horizontal well, the device comprising:
设置模块，用于按井斜角对水平井进行分段；A setting module is used to segment horizontal wells according to well inclination;
参数确定模块，用于根据原始录井数据确定水平井各段的岩屑浓度影响参数；A parameter determination module is used to determine the cuttings concentration influencing parameters of each section of the horizontal well based on the original logging data;
函数建立模块，用于建立各段对应的岩屑浓度影响参数与岩屑浓度的非线性预测函数；A function building module is used to build a nonlinear prediction function of the cuttings concentration influencing parameters and cuttings concentration corresponding to each section;
预测模块，用于利用所述预测函数对水平井环空岩屑浓度进行预测。The prediction module is used to predict the cuttings concentration in the annulus of the horizontal well using the prediction function.
可选地，所述参数确定模块包括：Optionally, the parameter determination module includes:
数据获取单元，用于获取水平井各段的原始录井数据；A data acquisition unit, used to acquire original logging data of each section of the horizontal well;
标准化单元，用于根据所述原始录井数据生成维标准化样本矩阵，；A standardization unit is used to generate a dimensional standardized sample matrix, ;
系数确定单元，用于确定所述维标准化样本矩阵的相关系数矩阵；A coefficient determination unit is used to determine the The correlation coefficient matrix of the dimensionally standardized sample matrix;
特征向量确定单元，用于根据所述相关系数矩阵得到个特征向量；A feature vector determination unit is used to obtain a feature vector according to the correlation coefficient matrix. feature vectors;
参数选择单元，用于利用所述个特征向量和所述维标准化样本矩阵，确定多个主成分作为岩屑浓度影响参数。A parameter selection unit is used to utilize the The eigenvectors and The dimensional standardized sample matrix was used to determine multiple principal components as the influencing parameters of cuttings concentration.
可选地，所述函数建立模块包括：Optionally, the function establishment module includes:
样本获取单元，用于获取对应所述岩屑浓度影响参数的样本数据；A sample acquisition unit, used to acquire sample data corresponding to the cuttings concentration influencing parameter;
样本处理单元，用于对所述样本数据进行归一化处理，得到处理后的样本数据；A sample processing unit, used for performing normalization processing on the sample data to obtain processed sample data;
模型训练单元，用于利用所述处理后的样本数据训练BP神经网络模型，所述BP神经网络的输入节点为所述多个主成分对应的特征向量，输出为岩屑浓度预测结果。The model training unit is used to train a BP neural network model using the processed sample data, wherein the input nodes of the BP neural network are the characteristic vectors corresponding to the plurality of principal components, and the output is a prediction result of the cuttings concentration.
一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器运行时执行所述水平井环空岩屑浓度预测方法的步骤。A computer-readable storage medium stores a computer program, which executes the steps of the method for predicting cuttings concentration in the annulus of a horizontal well when the computer program is executed by a processor.
本发明提供的水平井环空岩屑浓度预测方法及装置，按照不同岩屑运移机理，按照井斜角对水平井进行分段，利用主成分分析方法得到不同井斜段岩屑浓度影响因素最佳特征参数；通过人工神经网络预测方法，实现岩屑浓度影响因素与岩屑浓度非线性关系映射，得到岩屑浓度预测拟合函数，从而实现了不同井斜段水平井环空岩屑浓度预测，提高了环空岩屑浓度预测准确定和适应性。The method and device for predicting cuttings concentration in the annulus of a horizontal well provided by the present invention divide the horizontal well into sections according to different cuttings migration mechanisms and well inclination angles, and use the principal component analysis method to obtain the optimal characteristic parameters of cuttings concentration influencing factors in different well inclination sections; through the artificial neural network prediction method, the nonlinear relationship mapping between cuttings concentration influencing factors and cuttings concentration is achieved, and a cuttings concentration prediction fitting function is obtained, thereby realizing the prediction of cuttings concentration in the annulus of horizontal wells with different well inclination sections, and improving the accuracy and adaptability of the annulus cuttings concentration prediction.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施方式或现有技术中的技术方案，下面将对实施方式中所需要使用的附图作简单地介绍。显而易见地，下面描述中的附图仅仅是本发明的一些实施方式，对于本领域普通技术人员而言，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
图1是本发明提供的水平井环空岩屑浓度预测方法的一种流程图；FIG1 is a flow chart of a method for predicting cuttings concentration in annular space of a horizontal well provided by the present invention;
图2是本发明实施例中确定岩屑浓度影响参数的一种流程图；FIG2 is a flow chart of determining parameters affecting cuttings concentration according to an embodiment of the present invention;
图3是本发明实施例中建立岩屑浓度影响参数与岩屑浓度的非线性预测函数的流程图；3 is a flow chart of establishing a nonlinear prediction function of cuttings concentration influencing parameters and cuttings concentration in an embodiment of the present invention;
图4是本发明实施例中主成分及其特征值的示意图；FIG4 is a schematic diagram of a principal component and its eigenvalues in an embodiment of the present invention;
图5是本发明实施例中主成分及其贡献率及累积贡献率的示意图；FIG5 is a schematic diagram of the main components and their contribution rates and cumulative contribution rates in an embodiment of the present invention;
图6是利用本发明方法对环空岩屑浓度预测结果与实际岩屑浓度对比示意图；FIG6 is a schematic diagram showing a comparison between the predicted results of the annular cuttings concentration and the actual cuttings concentration using the method of the present invention;
图7是本发明提供的水平井环空岩屑浓度预测装置的一种结构示意图。FIG. 7 is a schematic structural diagram of a device for predicting cuttings concentration in annular space of a horizontal well provided by the present invention.
具体实施方式DETAILED DESCRIPTION
以下结合附图对本发明的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本发明，并不用于限制本发明。The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分的实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明保护的范围。In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.
针对现有环空岩屑浓度预测存在的问题，本发明提供一种水平井环空岩屑浓度预测方法及装置，按照不同岩屑运移机理，按照井斜角对水平井进行分段，利用主成分分析方法得到不同井斜段岩屑浓度影响因素最佳特征参数；通过神经网络预测方法，实现岩屑浓度影响因素与岩屑浓度非线性关系映射，得到岩屑浓度预测拟合函数，从而实现不同井斜段水平井环空岩屑浓度预测。In view of the problems existing in the existing prediction of cuttings concentration in the annulus, the present invention provides a method and device for predicting cuttings concentration in the annulus of a horizontal well. According to different cuttings migration mechanisms and well inclination angles, the horizontal well is segmented, and the principal component analysis method is used to obtain the optimal characteristic parameters of the factors affecting cuttings concentration in different well inclination sections. Through the neural network prediction method, the nonlinear relationship mapping between the factors affecting cuttings concentration and cuttings concentration is achieved, and the cuttings concentration prediction fitting function is obtained, thereby realizing the prediction of cuttings concentration in the annulus of horizontal wells with different well inclination sections.
影响岩屑浓度因素的参数有很多，其中包括可以调节的“控制参数”，也包括“受影响参数”，任何控制参数的变化都会影响“受影响参数”的变化。对于井眼清洁相关参数来讲，受影响参数包括“内部状态参数”和“可输出参数”。There are many parameters that affect the cuttings concentration, including adjustable "control parameters" and "affected parameters". Any change in control parameters will affect the change in "affected parameters". For borehole cleaning related parameters, the affected parameters include "internal state parameters" and "output parameters".
在本发明实施例中，用于分析岩屑浓度的参数主要可以包括但不限于以下任意一种或多种：In the embodiment of the present invention, the parameters used to analyze the concentration of rock cuttings may mainly include but are not limited to any one or more of the following:
X1-井斜角（deviation angle，DEV），单位“度”：井斜角是指油水井中某点的中轴线与地球铅垂线之间的夹角，其范围为0°~180°，井斜角用来指示井眼轨迹的斜度。随着井斜角的增加，岩屑沿着钻井液流动方向的分量变小，岩屑沉降程度变大，环空岩屑浓度变大。X1-Deviation angle (DEV), unit "degree": Deviation angle refers to the angle between the central axis of a point in an oil and water well and the plumb line of the earth, ranging from 0° to 180°. Deviation angle is used to indicate the inclination of the wellbore trajectory. As the deviation angle increases, the weight of the cuttings along the flow direction of the drilling fluid decreases, the degree of cuttings settlement increases, and the concentration of cuttings in the annulus increases.
X2-钻井液密度（MDOA），单位“g/cm3”：钻井液密度是指单位体积钻井液的质量。提高钻井液密度可以降低岩屑浓度井，但是当达到一定程度后对岩屑浓度影响变弱。X2-Drilling fluid density (MDOA), unit "g/cm3": Drilling fluid density refers to the mass of drilling fluid per unit volume. Increasing drilling fluid density can reduce the concentration of cuttings, but when it reaches a certain level, the effect on the concentration of cuttings becomes weaker.
X3-钻井液中塑性粘度（PV），单位“mPa.s”，钻井液中塑性粘度是塑性流体的性质，它不随剪切速率而变化。塑性粘度反映了在层流情况下，钻井液中网架结构的破坏与恢复处于动平衡时，悬浮的固相颗粒之间、固相颗粒与液相之间以及连续液相内部的内摩擦作用的强弱。X3-Plastic viscosity (PV) in drilling fluid, unit "mPa.s", plastic viscosity in drilling fluid is the property of plastic fluid, it does not change with shear rate. Plastic viscosity reflects the strength of internal friction between suspended solid particles, between solid particles and liquid phase, and inside continuous liquid phase when the destruction and recovery of the grid structure in the drilling fluid are in dynamic equilibrium under laminar flow conditions.
X4-动切力（YP），单位“Pa”：动切力是反映钻井液流体在流动时内部凝胶网状结构的强度，也称屈服值。X4- Dynamic shear force (YP), unit "Pa": Dynamic shear force reflects the strength of the internal gel network structure of the drilling fluid when it flows, also known as the yield value.
增大PV/YP比值可以降低环空岩屑浓度，其影响程度与岩屑尺寸、转速、井倾角和钻杆偏心率有关。Increasing the PV/YP ratio can reduce the concentration of cuttings in the annulus, and the degree of influence is related to the cuttings size, rotation speed, well inclination and drill pipe eccentricity.
X5-钻时（ROPA），单位“min/m”：钻时是指每钻进一定厚度的岩层所需要的时间，钻时减小表示单位时间产生岩屑量增多，岩屑浓度增加。X5-Drilling time (ROPA), unit "min/m": drilling time refers to the time required to drill into a certain thickness of rock layer. A decrease in drilling time means an increase in the amount of rock cuttings produced per unit time and an increase in the concentration of rock cuttings.
X6-钻压（WOBA），单位“kN”：钻压表示钻头作用到地层轴向力，钻压越大钻时越小，岩屑浓度增大。X6-weight on bit (WOBA), unit "kN": The weight on bit indicates the axial force applied by the drill bit to the formation. The greater the weight on bit, the smaller the drilling time and the greater the concentration of cuttings.
X7-扭矩（TQA），单位“kN.m”：扭矩作为可输出参数，表示钻头作用到地层的力矩，扭矩越大钻时越小，岩屑浓度增大。X7-Torque (TQA), unit "kN.m": Torque is an output parameter, which indicates the moment of force applied by the drill bit to the formation. The greater the torque, the smaller the drilling time and the greater the cuttings concentration.
X8-转速（RPMA），单位“r/min”：转速作为控制参数影响着扭矩，增加转速可以扰动环空岩屑，提高岩屑流动效率，降低岩屑浓度，X8-Speed (RPMA), unit "r/min": Speed as a control parameter affects torque. Increasing the speed can disturb the annular cuttings, improve the cuttings flow efficiency, and reduce the cuttings concentration.
X9-立管压力（SPPA），单位“MPa”：立管压力作为可输出参数，立管压力可以间接反映随着钻井液流动的岩屑数量，立管压力越大，环空岩屑浓度越小。X9-Standpipe pressure (SPPA), unit "MPa": Standpipe pressure is an output parameter. Standpipe pressure can indirectly reflect the amount of cuttings flowing with the drilling fluid. The greater the standpipe pressure, the smaller the concentration of cuttings in the annulus.
X10-钻井液出口流量（MFOP），单位“%”：钻井液出口流量是相对流量，表示环空钻井液流动量。当发生钻井液漏失出口流量时，随着钻井液流动岩屑减少，环空岩屑浓度增大。X10-Drilling fluid outlet flow rate (MFOP), unit "%": Drilling fluid outlet flow rate is a relative flow rate, indicating the flow of drilling fluid in the annulus. When the drilling fluid loss outlet flow rate occurs, the concentration of cuttings in the annulus increases as the drilling fluid flows and the cuttings decrease.
X11-钻井液入口流量（MFIA），单位“L/s”：钻井液入口流量是绝对流量，是钻井液从进管口进入钻井泵的流量。钻井液入口流量作为控制参数影响着钻井液环空流速，环空流速越大岩屑浓度越小。X11-Drilling fluid inlet flow rate (MFIA), unit "L/s": Drilling fluid inlet flow rate is the absolute flow rate, which is the flow rate of drilling fluid entering the drilling pump from the inlet. The drilling fluid inlet flow rate, as a control parameter, affects the annular flow rate of the drilling fluid. The greater the annular flow rate, the lower the cuttings concentration.
X12-钻井液出口温度（MTOA），单位“℃”：钻井液出口温度影响着钻井液性能偏心率具有显著的负面影响。X12-Drilling fluid outlet temperature (MTOA), unit "℃": Drilling fluid outlet temperature affects drilling fluid performance. Eccentricity has a significant negative impact.
针对划分的水平进的各段，可以选择上述全部或部分参数作为岩屑浓度影响参数，分段基于选出的岩屑浓度影响参数建立岩屑浓度影响参数与岩屑浓度的非线性预测函数，从而可以使该预测函数能够更好地适应相应段的特点，更准确地预测水平井环空岩屑浓度。For each divided horizontal section, all or part of the above parameters can be selected as cuttings concentration influencing parameters, and a nonlinear prediction function of cuttings concentration influencing parameters and cuttings concentration can be established based on the selected cuttings concentration influencing parameters, so that the prediction function can better adapt to the characteristics of the corresponding section and more accurately predict the cuttings concentration in the annulus of the horizontal well.
如图1所示，是本发明提供的水平井环空岩屑浓度预测方法的一种流程图，包括以下步骤：As shown in FIG1 , it is a flow chart of a method for predicting cuttings concentration in annular space of a horizontal well provided by the present invention, comprising the following steps:
步骤101，按井斜角对水平井进行分段。Step 101, segmenting the horizontal well according to the well inclination angle.
比如，按井斜角0-30°、30-65°和65+°对水平井进行分段。For example, horizontal wells are segmented according to the well inclination angles of 0-30°, 30-65° and 65+°.
当然，上述分段只是示例性说明，在具体实施中，可以根据需要进行不同粒度的分段，对此本发明不做限定。Of course, the above segmentation is only an exemplary description. In a specific implementation, segmentation of different granularities can be performed as needed, and the present invention does not limit this.
步骤102，根据原始录井数据确定水平井各段的岩屑浓度影响参数。Step 102, determining the cuttings concentration influencing parameters of each section of the horizontal well according to the original logging data.
由于同一参在不同井斜角的情况下对岩屑浓度的影响作用程度会有所不同，为此，本发明实施例中，可以分别针对各段来确定该段的岩屑浓度影响参数，具体过程将在后面详细说明。Since the same parameter has different effects on the cuttings concentration at different well inclination angles, in the embodiment of the present invention, the cuttings concentration influencing parameter of each section can be determined respectively, and the specific process will be described in detail later.
步骤103，建立各段对应的岩屑浓度影响参数与岩屑浓度的非线性预测函数。Step 103: Establish a nonlinear prediction function of the cuttings concentration influencing parameters corresponding to each section and the cuttings concentration.
在一种非限制性实施例中，可以基于所述岩屑浓度影响参数建立基于BP神经网络模型的非线性预测函数，也就是说，根据确定的岩屑浓度影响参数采集样本数据，利用所述样本数据训练BP神经网络模型，所述BP神经网络的输入节点为n个主成分（即步骤102中确定的n个岩屑浓度影响参数）对应的特征向量，输出为岩屑浓度预测结果。In a non-limiting embodiment, a nonlinear prediction function based on a BP neural network model can be established based on the cuttings concentration influencing parameters, that is, sample data is collected according to the determined cuttings concentration influencing parameters, and the BP neural network model is trained using the sample data. The input nodes of the BP neural network are the characteristic vectors corresponding to the n principal components (i.e., the n cuttings concentration influencing parameters determined in step 102), and the output is the cuttings concentration prediction result.
步骤104，利用所述预测函数对水平井环空岩屑浓度进行预测。Step 104: Use the prediction function to predict the cuttings concentration in the annulus of the horizontal well.
在进行预测时，需要对水平井进行分段预测，针对每段，需要选用与该段对应的预测函数，以保证预测结果的准确性。When making predictions, it is necessary to make segmented predictions for the horizontal wells. For each segment, it is necessary to select the prediction function corresponding to the segment to ensure the accuracy of the prediction results.
在上述步骤102中，需要根据原始录井数据确定水平井各段的岩屑浓度影响参数，针对每段，确定岩屑浓度影响参数的方法相同，下面结合图2进行详细说明。In the above step 102, it is necessary to determine the cuttings concentration influencing parameters of each section of the horizontal well according to the original logging data. For each section, the method for determining the cuttings concentration influencing parameters is the same, which is described in detail below in conjunction with FIG. 2 .
如图2所示，是本发明实施例中确定岩屑浓度影响参数的一种流程图，包括以下步骤：As shown in FIG. 2 , it is a flow chart of determining the parameters affecting the cuttings concentration according to an embodiment of the present invention, including the following steps:
步骤201，获取水平井各段的原始录井数据。Step 201, obtaining original logging data of each section of the horizontal well.
步骤202，根据所述原始录井数据生成维标准化样本矩阵，。Step 202: Generate a dimensional standardized sample matrix, .
由于原始录井数据是依时间顺序采集的数据，因此根据所述原始录井数据生成维样本矩阵，然后对所述维样本矩阵中的阵元进行标准化变换，得到维标准化样本矩阵。Since the original logging data is collected in time sequence, the dimensional sample matrix, and then The array elements in the dimensional sample matrix are normalized and transformed to obtain dimensional standardized sample matrix.
需要说明的是，的值可以根据实际的录井数据中参数的数量（即参数的个数）来确定，比如=12，对此本发明实施例不做限定。It should be noted that The value of can be determined according to the number of parameters in the actual logging data (i.e. the number of parameters), for example =12, which is not limited in this embodiment of the present invention.
比如，将原始录井数据中每条数据记录表示为：For example, each data record in the original logging data is represented as:
，，n是数据记录行数总数； , ,n is the total number of data record rows;
根据上述每条数据记录可以得到维样本矩阵，表示为：According to each of the above data records, we can get dimensional sample matrix, expressed as:
； ;
对维样本矩阵中的各阵元进行如下标准化变换：right Each element in the dimensional sample matrix undergoes the following standardized transformation:
； ;
其中，为维样本矩阵中第j参数列的平均值，；为维样本矩阵中每个参数的方差，；in, for The average value of thej -th parameter column in the dimensional sample matrix, ; for The variance of each parameter in the dimensional sample matrix, ;
通过对各阵元进行标准化处理，得到维标准化样本矩阵。By standardizing each array element, we can obtain Dimensional standardized sample matrix .
步骤203，确定所述维标准化样本矩阵的相关系数矩阵。Step 203, determine the The correlation coefficient matrix of the dimensional standardized sample matrix.
具体地，可以按照以下公式确定标准化样本矩阵的相关系数矩阵：Specifically, the standardized sample matrix can be determined according to the following formula: The correlation coefficient matrix of
； ;
其中，为标准化样本矩阵的转置矩阵。in, is the standardized sample matrix The transposed matrix of .
步骤204，根据所述相关系数矩阵得到个特征向量。Step 204, obtaining according to the correlation coefficient matrix feature vectors.
对于相关系数矩阵，可以得到个非负特征根，和对应于特征根的个单位化特征向量，构成一个正交矩阵，此时变量之间无任何相关性，也即没有任何冗余信息，用表示如下：For the correlation matrix , we can get Non-negative characteristic roots , and corresponding to the characteristic root Unitized eigenvectors form an orthogonal matrix. At this time, there is no correlation between the variables, that is, there is no redundant information. It is expressed as follows:
其中，正交矩阵的每一列为对应于特征值的特征向量，从而得到个特征向量。Among them, the orthogonal matrix Each column of is the eigenvector corresponding to the eigenvalue, so we get feature vectors.
步骤205，利用所述个特征向量和所述维标准化样本矩阵，确定多个主成分作为岩屑浓度影响参数。Step 205, using the The eigenvectors and The dimensional standardized sample matrix was used to determine multiple principal components as the influencing parameters of cuttings concentration.
可以根据所述个特征向量和所述相关系数矩阵求解特征方程，得到个主成分；然后依据从大到小原则从个主成分中选出多个主成分作为岩屑浓度影响参数。According to the The characteristic vectors and the correlation coefficient matrix are used to solve the characteristic equation and we get principal components; then according to the principle of descending from large to small Multiple principal components are selected from the principal components as the influencing parameters of cuttings concentration.
比如，对上述相关系数矩阵，解其特征方程，得到个主成分如下：For example, for the above correlation coefficient matrix , solve its characteristic equation ,get The principal components are as follows:
通过特征向量与所述相关系数矩阵，可以求出个主成分。Through the eigenvector and the correlation coefficient matrix, we can find principal components.
根据上式可以看出第几个主成分实际上就是正交矩阵的第几列（即最大特征值所对应的特征向量）与标准化矩阵的第几列分别相乘再相加所得，同理可得个主成分。According to the above formula, we can see the number of principal components In fact, it is an orthogonal matrix The nth column of (i.e., the eigenvector corresponding to the largest eigenvalue) is multiplied and added with the nth column of the normalized matrix. Similarly, we can get principal components.
然后，从个主成分中选出个主成分，将个主成分中的参数作为岩屑浓度影响参数。在选择时，可以根据各主成分的贡献率来选择。比如，可以将贡献率大于设定阈值的主成分作为岩屑浓度影响参数；或者按贡献率由大到小排序，从中选出（）个主成分作为岩屑浓度影响参数；或者按贡献率由大到小排序，从中选出累计贡献率大于设定的累计阈值的多个主成分作为岩屑浓度影响参数。Then, from Select from the principal components The principal components The parameters in the principal components are used as the parameters affecting the rock cuttings concentration. When selecting, you can choose according to the contribution rate of each principal component. For example, you can use the principal component with a contribution rate greater than the set threshold as the parameter affecting the rock cuttings concentration; or you can sort the principal components from large to small according to the contribution rate and select （ ) principal components as the influencing parameters of cuttings concentration; or sort them from large to small according to contribution rate, and select multiple principal components whose cumulative contribution rate is greater than the set cumulative threshold as the influencing parameters of cuttings concentration.
主成分的贡献率可以定义为总方差中属于主成分的比例，可表示为：。Principal Components The contribution rate of the principal component can be defined as The ratio can be expressed as: .
前个主成分的贡献率之和，称为主成分的累积贡献率。forward The sum of the contribution rates of the principal components , called the cumulative contribution rate of the principal component.
通常取较小的，可使得累积贡献率≥80%。从而达到降维的目的，而较少损失的信息。Usually take the smaller , the cumulative contribution rate can be made ≥ 80%. This achieves the purpose of dimensionality reduction with less information loss.
如图3所示，是本发明实施例中建立岩屑浓度影响参数与岩屑浓度的非线性预测函数的流程图，包括以下步骤：As shown in FIG3 , it is a flow chart of establishing a nonlinear prediction function of cuttings concentration influencing parameters and cuttings concentration in an embodiment of the present invention, which includes the following steps:
步骤301，获取对应所述岩屑浓度影响参数的样本数据。Step 301, obtaining sample data corresponding to the cuttings concentration influencing parameter.
步骤302，对所述样本数据进行归一化处理，得到处理后的样本数据。Step 302: normalize the sample data to obtain processed sample data.
由于不同参数具有不同量纲，因此需采用线性归一化方法对数据进行处理，将数据转换为[0，1]之间的值，使后续网络训练时各输入分量具有同等的地位，保证较为完善的网络训练环境。Since different parameters have different dimensions, it is necessary to use a linear normalization method to process the data and convert the data into values between [0, 1] so that each input component has an equal status during subsequent network training, ensuring a more complete network training environment.
线性归一化处理的计算公式如下：The calculation formula for linear normalization is as follows:
式中，、、、分别表示各组样本信息中的初始样本数据、归一化后的数据、初始样本数据中的最小值及最大值。In the formula, , , , They respectively represent the initial sample data, normalized data, and the minimum and maximum values in the initial sample data in each group of sample information.
步骤303，利用所述处理后的样本数据训练BP神经网络模型，所述BP神经网络的输入节点为所述多个主成分对应的特征向量，输出为岩屑浓度预测结果。Step 303: Use the processed sample data to train a BP neural network model, where the input nodes of the BP neural network are the eigenvectors corresponding to the plurality of principal components, and the output is a prediction result of the cuttings concentration.
BP神经网络输入层节点数为，即确定的岩屑浓度影响参数的个数，相应的隐含层节点数。需要输出的结果是岩屑浓度，则BP神经网络输出层节点数为1。The number of nodes in the input layer of the BP neural network is , that is, the number of parameters affecting the cuttings concentration determined, and the corresponding number of hidden layer nodes The output result is the cuttings concentration, so the number of nodes in the BP neural network output layer is 1.
所述BP神经网络隐含层各神经元的输入函数和输出函数分别为：The input function and output function of each neuron in the hidden layer of the BP neural network are:
其中，为输入函数，为输出函数；为输入参数，为输入参数总数，为第个输入参数的第一列权重，为偏置值，其初始值可以随机确定，后续可以根据采用的算法自动调整；表示的分布函数。in, is the input function, is the output function; is the input parameter, is the total number of input parameters, For the The first column weights of the input parameters, is the bias value, whose initial value can be randomly determined and can be automatically adjusted later according to the adopted algorithm; express The distribution function of .
输出层为岩屑浓度预测结果，输出层神经元函数值为：The output layer is the prediction result of cuttings concentration, and the neuron function value of the output layer is:
其中，为权重值，为偏置值，其初始值可以随机确定，后续可以根据采用的算法自动调整。in, is the weight value, It is the bias value, whose initial value can be randomly determined and can be automatically adjusted later according to the adopted algorithm.
岩屑浓度预测结果和实际测量数据之间存在误差，在本发明实施例中，训练样本的总误差函数可以为：There is an error between the predicted result of cuttings concentration and the actual measured data. In the embodiment of the present invention, the total error function of the training sample can be:
其中，是总误差值；是标准数据期望输出值；是实际输出值，P表示主成分个数，L表示隐含层节点数。in, is the total error value; is the expected output value of the standard data; is the actual output value, P represents the number of principal components, and L represents the number of hidden layer nodes.
根据误差梯度下降法依次计算输出层权值的修正量；输出层阈值的修正量；隐含层权值的修正量；隐含层阈值的修正量：According to the error gradient descent method, the correction amount of the output layer weight is calculated in sequence ; Correction value of output layer threshold ; Correction value of hidden layer weight ; Correction value of hidden layer threshold :
其中，为学习速率；为网络权值变化率；为网络误差变化率。in, is the learning rate; is the rate of change of network weights; is the network error change rate.
神经网络中的各权值、阈值经过误差反向传播而不断进行修正，直至总误差满足其规定的精度，则完成训练。Each weight and threshold in the neural network is continuously modified through error back propagation until the total error If the specified accuracy is met, the training is completed.
本发明提供的水平井环空岩屑浓度预测方法及装置，按照不同岩屑运移机理，对不同井斜进行分段，利用主成分分析方法得到不同井斜段岩屑浓度影响因素最佳特征参数；通过人工神经网络预测方法，实现岩屑浓度影响因素与岩屑浓度非线性关系映射，得到了岩屑浓度预测拟合函数，实现了不同井斜段水平井环空岩屑浓度预测，提高了环空岩屑浓度预测准确定和适应性。The method and device for predicting cuttings concentration in the annulus of a horizontal well provided by the present invention divide different well inclinations into sections according to different cuttings migration mechanisms, and use the principal component analysis method to obtain the optimal characteristic parameters of cuttings concentration influencing factors in different well inclination sections; through the artificial neural network prediction method, the nonlinear relationship mapping between cuttings concentration influencing factors and cuttings concentration is achieved, and a cuttings concentration prediction fitting function is obtained, thereby realizing the prediction of cuttings concentration in the annulus of horizontal wells with different well inclination sections, and improving the accuracy and adaptability of the annulus cuttings concentration prediction.
利用本发明方案，可以对不同井斜段自动选取分析参数，基于机器学习方法建立输入和输出非线性拟合函数，智能化预测环空岩屑浓度的方法，提高了水平井井眼清洁评价的准确性。By utilizing the scheme of the present invention, analysis parameters can be automatically selected for different well deviation sections, input and output nonlinear fitting functions can be established based on machine learning methods, and a method for intelligently predicting annular cuttings concentration can be developed, thereby improving the accuracy of horizontal wellbore cleaning evaluation.
下面以一口实际的水平井，进一步详细说明本发明方案。The present invention is further described in detail below using an actual horizontal well.
水平井根据井斜角的不同将井眼清洁分为3种。在不同的井斜角范围内，岩屑运输以及井眼清洁策略都会有很大的不同。比如：Horizontal wells are divided into three types of wellbore cleaning according to the different well inclination angles. In different well inclination angles, cuttings transportation and wellbore cleaning strategies will be very different. For example:
在0-30°井段，通过克服岩屑沉降速度，将岩屑带到地面，在这种情况下，岩屑需要下降千米多才能到达井底。井眼清洁是由钻井液的粘度和流速简单地提供的。当泵关闭时，岩屑被粘稠的钻井液悬浮起来，尽管随着时间的推移会有一些沉淀。In the 0-30° well section, cuttings are brought to the surface by overcoming the cuttings settling velocity, in which case the cuttings need to descend more than a kilometer to reach the bottom of the well. Hole cleaning is simply provided by the viscosity and flow rate of the drilling fluid. When the pump is turned off, the cuttings are suspended by the viscous drilling fluid, although there will be some settling over time.
在30-65°井段中，岩屑开始形成 "沙丘"。岩屑在井筒内的移动主要是在井眼底边，但很容易被搅动到流动状态。这个倾角范围最显著的特点是，当泵被关闭时，"沙丘"将开始滑落（或雪崩）到井底。这大大改变了与垂直井相比的井眼清洁策略。In the 30-65° well section, the cuttings begin to form "dunes". The movement of cuttings in the wellbore is mainly at the bottom of the wellbore, but it is easily stirred into a flowing state. The most significant feature of this inclination range is that when the pump is turned off, the "dunes" will begin to slide (or avalanche) to the bottom of the well. This greatly changes the wellbore cleaning strategy compared to vertical wells.
在65+°井段中，岩屑落在在井眼底边，形成一个长而连续的岩屑床。所有的钻井液都会在钻杆上方移动，需要机械搅拌来移动岩屑，无论泥浆的流速或粘度如何。尽管与雪崩沙丘相关的挑战已经消失，但在这种环境下的井眼清洁工作实际上更困难。In the 65+° section, cuttings fall to the bottom of the wellbore, forming a long, continuous cuttings bed. All the drilling fluid moves above the drill pipe, requiring mechanical agitation to move the cuttings, regardless of the mud flow rate or viscosity. Although the challenges associated with avalanche dunes are gone, hole cleaning in this environment is actually more difficult.
因此，按照井斜0-30°、30-65°和65+°对井斜进行分段。Therefore, the well inclination is divided into sections according to well inclination 0-30°, 30-65° and 65+°.
下面以0-30°井斜段说明具体实施过程，对于30-65°和65+°井段同理。具体实施过程如下：The following is an example of the specific implementation process using the 0-30° well inclination section. The same is true for the 30-65° and 65+° well sections. The specific implementation process is as follows:
一、利用主成分分析方法对不同井斜段环空岩屑浓度影响参数进行降维1. Use principal component analysis to reduce the dimension of the parameters affecting the concentration of cuttings in the annulus of different well inclination sections
（1）原始录井实时数据处理(1) Real-time data processing of raw logging
以实际井数据作为训练样本，获取样本数量360条，其中0-30°井斜段样本数量120条，30-65°井斜段样本120条，65+°井斜段样本数量120条，井数据和各个参数值范围，测试样本数据情况如表1所示。Taking the actual well data as training samples, 360 samples were obtained, including 120 samples in the 0-30° well inclination section, 120 samples in the 30-65° well inclination section, and 120 samples in the 65+° well inclination section. The well data and the range of various parameter values, as well as the test sample data are shown in Table 1.
表1Table 1
（2）计算相关系数矩阵R的特征值及相应的特征向量(2) Calculate the eigenvalues and corresponding eigenvectors of the correlation coefficient matrix R
为每一个参数进行主成分编号，见表1，计算相关系数矩阵R的特征值，计算结果如图4所示。The principal component number is assigned to each parameter, as shown in Table 1, and the eigenvalue of the correlation coefficient matrix R is calculated. The calculation results are shown in Figure 4.
（3）计算和选择主成分(3) Calculation and selection of principal components
计算各主成分的贡献率，进一步还可计算累计贡献率，计算结果如图5所示。The contribution rate of each principal component is calculated, and the cumulative contribution rate can be further calculated. The calculation results are shown in Figure 5.
对于第一主成分X1（DEV）、X2（MDOA）、X3（PV）和X4（YP）指标项对其影响较大；第二主成分中X8（RPMA）和X11（MFIA）指标项对其影响较大；第三主成分中X5（ROPA）、X6（WOBA）、X7（TQA）和X9（SPP）指标项对其影响程度较大；第四主成分X6（WOBA）指标项对其影响程度较大；第五主成分X7（TQA）和X9（SPP）对其影响程度较大，因此确定了X1（DEV）、X2（MDOA）、X3（PV）、X4（YP）、X5（ROPA）、X6（WOBA）、X7（TQA）、X8（RPMA）、X9（SPP）和X11（MFIA）等10个指标作为后续神经网络模型0-30°井斜段岩屑浓度评价输入指标。For the first principal component, the index items X1 (DEV), X2 (MDOA), X3 (PV) and X4 (YP) have a greater impact on it; in the second principal component, the index items X8 (RPMA) and X11 (MFIA) have a greater impact on it; in the third principal component, the index items X5 (ROPA), X6 (WOBA), X7 (TQA) and X9 (SPP) have a greater impact on it; the index item X6 (WOBA) of the fourth principal component has a greater impact on it; the fifth principal component X7 (TQA) and X9 (SPP) have a greater impact on it. Therefore, 10 indicators including X1 (DEV), X2 (MDOA), X3 (PV), X4 (YP), X5 (ROPA), X6 (WOBA), X7 (TQA), X8 (RPMA), X9 (SPP) and X11 (MFIA) were determined as the input indicators for the evaluation of cuttings concentration in the 0-30° well inclination section of the subsequent neural network model.
利用主成分分析方法对不同井斜段样本数据进行分析，确定了不同井斜段的神经网络输入指标。The principal component analysis method was used to analyze the sample data of different well deviation sections, and the neural network input indicators of different well deviation sections were determined.
针对不同井斜段样本选取100条样本数据作为训练集进行拟合模型，选取20条样本数据作为测试集进行模型预测。For samples of different well deviation sections, 100 sample data were selected as training sets for model fitting, and 20 sample data were selected as test sets for model prediction.
神经网络模型测试采用最小误差精度0.001，最大迭代次数10000次，学习速率0.9，以小于最小精度和达到最大迭代次数满足条件之一作为停止训练条件。The neural network model test uses a minimum error accuracy of 0.001, a maximum number of iterations of 10,000 times, and a learning rate of 0.9. The training is stopped when either the minimum accuracy or the maximum number of iterations is met.
训练完成后，利用测试集对模型进行测试，对0-30°井斜段的环空岩屑浓度预测结果与实际岩屑浓度对比如图6所示。After the training is completed, the model is tested using the test set. The comparison between the predicted results of the annular cuttings concentration in the 0-30° well inclination section and the actual cuttings concentration is shown in Figure 6.
相应地，本发明实施例还提供一种水平井环空岩屑浓度预测装置，如图7所示，该水平井环空岩屑浓度预测装置700包括以下各模块：Accordingly, an embodiment of the present invention further provides a device for predicting cuttings concentration in the annulus of a horizontal well. As shown in FIG7 , the device 700 for predicting cuttings concentration in the annulus of a horizontal well includes the following modules:
设置模块701，用于按井斜角对水平井进行分段；Setting module 701, for segmenting the horizontal well according to the well inclination angle;
参数确定模块702，用于根据原始录井数据确定水平井各段的岩屑浓度影响参数；The parameter determination module 702 is used to determine the cuttings concentration influencing parameters of each section of the horizontal well according to the original logging data;
函数建立模块703，用于建立各段对应的岩屑浓度影响参数与岩屑浓度的非线性预测函数；Function establishment module 703, used to establish a nonlinear prediction function of the cuttings concentration influencing parameter and the cuttings concentration corresponding to each section;
预测模块704，用于利用所述预测函数对水平井环空岩屑浓度进行预测。The prediction module 704 is used to predict the cuttings concentration in the annulus of the horizontal well using the prediction function.
其中，上述参数确定模块702的一种具体实现结构可以包括以下各单元：A specific implementation structure of the parameter determination module 702 may include the following units:
数据获取单元，用于获取水平井各段的原始录井数据；A data acquisition unit, used to acquire original logging data of each section of the horizontal well;
标准化单元，用于根据所述原始录井数据生成维标准化样本矩阵，；A standardization unit is used to generate a dimensional standardized sample matrix, ;
系数确定单元，用于确定所述维标准化样本矩阵的相关系数矩阵；A coefficient determination unit is used to determine the The correlation coefficient matrix of the dimensionally standardized sample matrix;
特征向量确定单元，用于根据所述相关系数矩阵得到个特征向量；A feature vector determination unit is used to obtain a feature vector according to the correlation coefficient matrix. feature vectors;
参数选择单元，用于利用所述个特征向量和所述维标准化样本矩阵，确定多个主成分作为岩屑浓度影响参数。A parameter selection unit is used to utilize the The eigenvectors and The dimensional standardized sample matrix was used to determine multiple principal components as the influencing parameters of cuttings concentration.
其中，上述函数建立模块703的一种具体实现结构可以包括以下各单元：A specific implementation structure of the function establishment module 703 may include the following units:
样本获取单元，用于获取对应所述岩屑浓度影响参数的样本数据；A sample acquisition unit, used to acquire sample data corresponding to the cuttings concentration influencing parameter;
样本处理单元，用于对所述样本数据进行归一化处理，得到处理后的样本数据；A sample processing unit, used for performing normalization processing on the sample data to obtain processed sample data;
模型训练单元，用于利用所述处理后的样本数据训练BP神经网络模型，所述BP神经网络的输入节点为所述多个主成分对应的特征向量，输出为岩屑浓度预测结果。The model training unit is used to train a BP neural network model using the processed sample data, wherein the input nodes of the BP neural network are the characteristic vectors corresponding to the plurality of principal components, and the output is a prediction result of the cuttings concentration.
关于上述各模块的具体实现方式可参照前面本发明可信联邦学习方法实施例中的描述，在此不再赘述。For the specific implementation methods of the above modules, please refer to the description in the embodiment of the trusted federated learning method of the present invention, which will not be repeated here.
需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明并不受所描述的动作顺序的限制，因为依据本发明，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定是本发明所必须的。It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
在本发明所提供的几个实施例中，应该理解到，所揭露的装置，可通过其它的方式实现。In several embodiments provided by the present invention, it should be understood that the disclosed device can be implemented in other ways.
本发明还提供了一种存储介质，所述存储介质为计算机可读存储介质，其上存储有计算机程序，所述计算机程序运行时可以执行图1或图2或图3中所示方法的部分或全部步骤。所述存储介质可以包括只读存储器（Read-Only Memory，ROM）、随机存取存储器（Random Access Memory，RAM）、磁盘或光盘等。存储介质还可以包括非挥发性存储器（non-volatile）或者非瞬态（non-transitory）存储器等。The present invention also provides a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is run, some or all of the steps of the method shown in Figure 1, Figure 2, or Figure 3 can be executed. The storage medium may include a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. The storage medium may also include a non-volatile memory (non-volatile) or a non-transitory memory, etc.
上述实施例可以全部或部分地通过软件、硬件、固件或其他任意组合来实现。当使用软件实现时，上述实施例可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令或计算机程序。在计算机上加载或执行所述计算机指令或计算机程序时，全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以为通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线或无线方式向另一个网站站点、计算机、服务器或数据中心进行传输。The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means.
以上对本发明实施例进行了详细介绍，本文中应用了具体实施方式对本发明进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及系统，其仅仅是本发明一部分的实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明保护的范围，本说明书内容不应理解为对本发明的限制。因此，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The embodiments of the present invention are described in detail above. The present invention is described in detail using specific implementation methods herein. The description of the above embodiments is only used to help understand the method and system of the present invention. It is only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should belong to the scope of protection of the present invention, and the content of this specification should not be understood as limiting the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
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| CN202411000757.0ACN118551180B (en) | 2024-07-25 | 2024-07-25 | Horizontal well annulus cuttings concentration prediction method and device and storage medium |
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