Patent Application
20230352122
The present invention relates to a computer-implemented method and system for predicting hydrocarbon fluid properties using machine-learning-based models. The present invention further relates to a computer-implemented method and system for generating equations of state (EoS) for a plurality of hydrocarbon fluids using machine-learning-based models.
Petroleum consists of a complex mixture of hydrocarbons of various molecular weights, plus other organic compounds. The exact molecular composition of petroleum varies widely from formation to formation. The proportion of hydrocarbons in the mixture is highly variable and ranges from as much as 97% by weight in the lighter oils to as little as 50% in the heavier oils and bitumens. The hydrocarbons in petroleum are mostly alkanes (linear or branched), cycloalkanes, aromatic hydrocarbons, or more complicated chemicals like asphaltenes. The other organic compounds in petroleum typically contain carbon dioxide (CO2), nitrogen, oxygen, and sulfur, and trace amounts of metals such as iron, nickel, copper, and vanadium.
Knowledge of hydrocarbon fluid properties are of importance in the oil and gas industry. The fluid properties are essential for calculating the amount of the hydrocarbons initially in place, for reservoir simulation and production forecasting as well as for well, completion, pipeline and surface facility design. For measuring hydrocarbon fluid properties, typically, the pressure-volume-temperature (PVT) properties are measured as a function of pressure. Therefore, PVT data for hydrocarbon fluid samples is necessary. This PVT data may be acquired at different location of the production system: e.g., well bottom hole, well tubing head, and at the outlet of the last separator stage. Once acquired, this PVT data is sent to the laboratory for analysis where the fluid properties are measured. Nevertheless, regardless of the number of PVT data for hydrocarbon fluid samples that are acquired and the laboratory measurements that are performed, a thermodynamic model is typically used, such as an equation of state (EoS) model that represents the phase behavior of the petroleum fluid in the reservoir and is used to predict the hydrocarbon fluid properties under the expected range of pressure and temperature covering the life of the reservoir and the whole production system. Once the EoS model is defined, the EoS model can be used to compute a wide array of properties of the petroleum fluid of the reservoir, such as gas-oil ratio (GOR) or condensate-gas ratio (CGR), density of each phase, volumetric factors and compressibility, and heat capacity and saturation pressure (bubble or dew point). Thus, the EoS model can be solved to obtain saturation pressure at a given temperature. Moreover, GOR, CGR, phase densities, and volumetric factors are byproducts of the EoS model. Other properties, such as heat capacity or viscosity, can also be derived in conjunction with the information regarding fluid composition. Furthermore, the EoS model can be extended with other reservoir evaluation techniques for compositional simulation of flow and production behavior of the petroleum fluid of the reservoir, as is known in the art.
To validate a typical EoS model from laboratory measurements, a minimum set of measurements are required e.g. compositional properties (mole fractions of the components (e.g. N2, H2S, CO2, C1, C2, C3, C4, C5, C7, C8, etc.) and pseudo-components (e.g. C7+, C12+, C20+ and C36+) as well as the molecular weight of the pseudo-components), constant composition expansion (CCE), constant volume depletion (CVD), differential liberation (DL) and multi-stage separator (MSS).
Building machine-learning-based models based on the PVT data enables the capture of trends and prediction of fluid models in a very heterogeneous thermodynamic system exhibiting highly non-linear behaviors. The PVT data for hydrocarbon fluid samples stored in an oil company database are often not complete and might be missing properties, both black oil and compositional properties. Therefore, it is required to have a clean and structured PVT database complete with all required properties. In the presence of a large set of the PVT data for hydrocarbon fluid samples, some of these PVT data may lack part of the fluid properties as they may not have been measured in the laboratory. In some scenarios, the compositional properties in the database are absent as no representative PVT data could be obtained. In this case, the list of measurements is restricted to some properties obtained at stock tank conditions. A large set of existing PVT data may be lacking the PVT granularity of the heavy end of the hydrocarbon spectrum (e.g. they may be restricted to C7+). Companies with multiple fields and reservoirs may have an extensive set of the EoS models built using different PVT data (with their corresponding laboratory analysis). When new PVT data is acquired for a new field or from a specific region of an existing field, is it necessary to measure similarities with the existing PVT data of hydrocarbon fluid samples to either map the sample to an existing fluid model or intelligently predict a new one.
Predicting the PVT data properties using machine learning or artificial intelligence is known in the art. With respect to predicting compositional properties from other compositional properties, Wang et al. (Wang, K., Zuo, Y. and Jalali, Y., Schlumberger Technology Corp, 2017. Prediction of Fluid Composition and/or Phase Behavior. U.S. patent application Ser. No. 15/193,519) propose and test machine learning algorithms to predict and/or calculate components mole fractions (CO2, C1, C2, C3, C4, C5 and C6+) as well as C6+ molecular weight from components weight fractions. Wang et al. describe two workflows. A first workflow starts from weight fractions of CO2, C1, C2, C3, C4, C5 and C6+, predicts using machine learning the mole fraction of C6+ and then calculates, using pertinent equations relating molar weight to mole fractions and mass fractions, the molecular weight of C6+ followed by calculating, using pertinent equations relating mole fractions to weight fractions, the mole fractions of CO2, C1, C2, C3, C4, C5. The other workflow starts from weight fractions of CO2, C1, C2, C3, C4, C5 and C6+ and predicts using machine learning the mole fractions of CO2, C1, C2, C3, C4, C5 and C6+.
The predicting of the PVT data properties is further described in Almashan, Meshal & Narusue, Yoshiaki & Morikawa, Hiroyuki. (2019). Estimating PVT Properties of Crude Oil Systems Based on a Boosted Decision Tree Regression Modelling Scheme with K-Means Clustering. Asia Pacific Oil & Gas Conference and Exhibition, 25 Oct. 2019, 10.2118/196453-MS. The use of modelling approaches using machine learning to predict the PVT data properties is disclosed. The model proposed in the document is used for predicting the bubble point pressure (Pb) and the oil formation volume factor at bubble point pressure (Bob) as a function of oil and gas specific gravity, solution gas-oil ratio, and reservoir temperature by a boosted decision tree regression (BDTR) predictive modelling scheme.
Oloso, Munirudeen & Hassan, M. G. & Bader-El-Den, Mohamed & Buick, James. (2017). Hybrid Functional Networks for Oil Reservoir PVT Characterisation. Expert Systems with Applications. 12 Jun. 2017. 87. 10.1016/j.eswa.2017.06.014 discloses a method and system for prediction of crude oil PVT data. The method and system are used for prediction of a bubblepoint pressure (Pb) and an oil formation volume factor at bubblepoint pressure (Bob). The modelling is done using K-means clustering and functional networks comprising machine learning algorithms and neural networks.
Elsebakhi, Emad. (2009). Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems. Computers & Geosciences. 35. 1817-1826. 10.1016/j.cageo.2007.10.016 describes the use of adaptive neuro-fuzzy inference systems for a prediction of PVT-properties. The focus of the El-Sebakhy publication lies in the comparison of different approaches for the prediction of PVT-properties.
As discussed above, oil companies with multiple fields and reservoirs may have multiple ones of the EoS models built using different PVT data for hydrocarbon fluid samples (with their corresponding laboratory analysis). One or more PVT data sets may be used to build and calibrate the EoS model. These EoS models are typically built to model volumetric properties and phase behavior of one or more reservoirs or even a region in one reservoir in a specific field. The EoS models are also necessary to build dynamic models of the subsurface reservoirs, and these EoS models are the drivers in understanding reservoir performance and optimizing field development plans. The representativity of the reservoir with accurate fluid models is a big challenge, costly job, and it requires intensive and rigorous analysis. Multiple challenges are encountered through the process. Different known EoS models are built by different experts without coordination between these experts. The known EoS models are then tuned using different tuning parameters due to the non-uniqueness of the tuning process. Furthermore, different component and pseudo-components grouping/lumping schemes are applied by different experts as, again, the grouping/lumping is not a unique process, by nature.
It is an object of the present invention to provide a computer-implemented method and system for predicting hydrocarbon fluid properties using machine-learning-based models. It is further an object of the present invention to provide an improved computer-implemented method and system for generating equations of state (EoS) for a plurality of hydrocarbon fluids from the predicted hydrocarbon fluid properties.
In view of the state of the known technology and in accordance with a first aspect of the present invention, a computer-implemented method for predicting hydrocarbon fluid properties using machine-learning-based models is described in this document. This method comprises the method steps of: receiving an incomplete set of pressure-volume-temperature (PVT) data for hydrocarbon fluid samples from a PVT data base, wherein the incomplete set of PVT data comprises black oil properties and compositional properties; reading the incomplete set of PVT data by a reader module; transforming the incomplete set of PVT data into a unified data structure by the reader module, wherein the unified data structure is used for storing items of data input from different sources in a unified way; selecting items of the PVT data from the transformed incomplete set of PVT data by the reader module; processing the selected items of the PVT data by a correlating module to identify a plurality of correlations in the selected items of the PVT data based on one or more of the fluid properties of the hydrocarbon fluid samples; clustering, using of at least one of a plurality of clustering schemes, the selected items of the PVT data into a plurality of clusters by a clustering module; and performing machine learning by a machine learning module on ones of the plurality of clusters to predict missing fluid properties in the transformed incomplete set of PVT data and thus to obtain a complete set of PVT data. The complete set of PVT data comprises the black oil properties and compositional properties of the incomplete set of PVT data, wherein the complete set of PVT data comprises the black oil properties and compositional properties of the incomplete set of PVT data and further comprises predicted items of data for the black oil properties and predicted compositional properties.
The computer-implemented method in accordance with the first aspect of the present invention provides a systematic methodology to complete PVT data for properties for hydrocarbon fluid in a consistent manner.
In another aspect, the step of performing machine learning by the machine learning module of the computer-implemented method further comprises the step of predicting of fluid properties for incomplete sets of PVT data for the hydrocarbon fluid samples.
In another aspect, the computer-implemented method comprises the step of plotting the machine learning predictions by the machine learning module.
In another aspect, the computer-implemented method comprises the step of comparing the identified plurality of correlation results with the machine learning predictions.
In another aspect, the step of clustering of the computer-implemented method further comprises the step of completing fluid composition including C12+, C20+ and C36+ mole fraction and molecular weight and/or completing black oil properties, including, in the following order: solution gas oil ratio (GOR), BO@Psat, and saturation pressure (Psat).
In view of the state of the known technology and in accordance with a second aspect of the present invention, a system for predicting hydrocarbon fluid properties using machine-learning-based models is disclosed. The system comprises a pressure-volume-temperature (PVT) data base, a reader module, a correlating module, a clustering module, and a machine learning module. The pressure-volume-temperature (PVT) data base provides an incomplete set of pressure-volume-temperature (PVT) data for hydrocarbon fluid samples, wherein the incomplete set of PVT data comprises ones of black oil properties and compositional properties. The reader module reads in an incomplete set of PVT data, wherein the reader module is configured to transform the incomplete set of PVT data into a unified data structure, and wherein the unified data structure is used for storing items of data input from different sources in a unified way, and wherein the reader module is configured to select items of the PVT data from the incomplete set of PVT data using exploratory data analysis (EDA). The correlating module for processes the selected items of the transformed incomplete set of PVT data to identify a plurality of correlations in the selected items of the PVT data based on one or more of the fluid properties of the hydrocarbon fluid samples. The clustering module clusters, using of at least one of a plurality of clustering schemes, the selected items of the transformed incomplete set of PVT data into a plurality of clusters. The machine learning module performs machine learning on ones of the plurality of clusters to predict missing fluid properties in the incomplete set of PVT data and thus to obtain a complete set of PVT data, wherein the predicted complete set of PVT data comprises the black oil properties and compositional properties of the incomplete set of PVT data and further comprises the predicted items of data for the ones of the black oil properties and predicted compositional properties.
In view of the state of the known technology and in accordance with a third aspect of the present invention, a computer-implemented method for generating equations of state (EoS) for a plurality of hydrocarbon fluids from a predicted complete set of PVT data is disclosed. The method comprises the method steps of: delumping pressure-volume-temperature (PVT) data for hydrocarbon fluid samples from the predicted complete set of PVT data to one of a set of detailed fluid components, or to a common set of components and pseudo-components; lumping the PVT data from the predicted complete set of PVT data into a pre-defined set of components and pre-defined set of pseudo-components to generate a plurality of equation of state (EoS) models; generating for the PVT samples on the PVT data set an EoS model using a same set of tuning parameters and thereby generating an EoS fluid model fingerprint for the hydrocarbon fluid samples; and associating properties of the hydrocarbon fluid samples with the generated EoS fluid model fingerprint.
The computer-implemented method in accordance with the third aspect of the present invention leverages advancement in data sciences to unlock hidden information, patterns and relationships from massive volume and variety of reservoir hydrocarbon fluid data. The developed AI based algorithms can be used to consistently validate the hydrocarbon fluid data (also called PVT data), cluster the data and build machine learning based fluid models, i.e. equation of state (EoS), for different fields and reservoirs based on massive reservoir fluid information. The computer-implemented method in accordance with the second aspect of the present invention ensures reservoir models are managed with a high degree of confidence and accuracy with quality fluid data, maximizing returns while minimizing costs associated with EoS models.
In another aspect, the computer-implemented method comprises the step of training machine learning models to predict an EoS fluid model fingerprint for new hydrocarbon fluid samples.
In another aspect, the received or provided incomplete set of PVT data for hydrocarbon fluid samples comprises black oil properties and compositional properties. The black oil properties comprise at least one of reservoir temperature, solution gas-oil ratio, oil API gravity, gas gravity, dead oil viscosity, saturation pressure, saturated bubble point oil formation factor at saturation pressure, fluid density at reservoir conditions or any other black oil property, fluid compressibility at reservoir conditions, viscosity at reservoir conditions, fluid density at reservoir conditions or any other black oil property, and wherein the compositional properties comprise at least one of mole fractions of the components, in particular N2, H2S, CO2, C1, C2, C3, C4, C5, C7, C8, and pseud-components, in particular C7+, C12+, C20+ and C36+ or any other pseudo-component as well as the molecular weight of the pseudo-components.
In another aspect, the step of associating of the computer-implemented method further comprises the step of clustering the selected items of the PVT data into a plurality of clusters for performing machine learning on each one of the plurality of clusters.
In another aspect, the computer-implemented method comprises the step of comparing the results from clustering of the selected items of the PVT data with the plurality of equations of state (EoS) models and with heatmaps applying EoS models on the selected items of the PVT data.
In another aspect, the step of clustering of the selected items of the PVT data further comprises the step of identifying of clusters to which PVT data belong.
In view of the state of the known technology and in accordance with a fourth aspect of the present invention, a system for generating equations of state (EoS) for a plurality of hydrocarbon fluids from a predicted complete set of PVT data is disclosed. The system comprises a first module, a second module, a third module and a fourth module. The first module delumps pressure-volume-temperature (PVT) data for hydrocarbon fluid samples from the predicted complete set of PVT data to one of a set of detailed fluid components, or to a common set of components and pseudo-components. The second module lumps the PVT data for hydrocarbon fluid sample into a pre-defined set of components and pre-defined set of pseudo-components to generate a plurality of equation of state (EoS) models. The third module generates for the hydrocarbon fluid samples in a PVT data base an EoS model using a same set of tuning parameters and thereby generating an EoS fluid model fingerprint for the hydrocarbon fluid samples. The fourth module associates properties of the hydrocarbon fluid samples with the generated EoS fluid model fingerprint.
The present invention has a great impact on the way fluid modeling is performed. The present invention provides greater insights and better understanding of oil and gas fields and reservoirs. Furthermore, the results of the methods presented in the present invention can be scaled in very dynamic way to other fields in other regions. The present methods are based on a smart technology that adapts to the physical variation across fluid systems and perform fluid modeling with the highest confidence and accuracy. The technology is extendable and subject to integration with other subsurface domains as a smart fluid laboratory.
Also, other objects, features, aspects and advantages of the disclosed method will become apparent to those skilled in the art from the following detailed description.
The invention will now be described on the basis of figures. It will be understood that the embodiments and aspects of the invention described in the figures are only examples and do not limit the protective scope of the claims in any way. The invention is defined by the claims and their equivalents. It will be understood that features of one aspect or embodiment of the invention can be combined with a feature of a different aspect or aspects of other embodiments of the invention. The present invention becomes more obvious when reading the following detailed descriptions of some examples as part of the disclosure under consideration of the enclosed drawings. Referring now to the attached drawings which form a part of this disclosure:
Selected embodiments are now described with reference to the drawings. It is apparent from this disclosure to a person skilled in the art of generic fluid hydrocarbon fluid properties that the following description of embodiments is provided for illustrative purposes only and is not intended to limit the invention defined by the claims attached and their equivalents.
The following nomenclature is used in the present document:
The method 100 further comprises the step of selecting in step 104 items of the PVT data from the incomplete set of PVT data by the reader module 302. The selecting 104 of the items of PVT data is done using exploratory data analysis (EDA). The exploratory data analysis is a method used for automatically analyzing items of data and grouping these items of data into sets of data. This grouping of the data is based on observed patterns within the items of PVT data. These observed patterns in the items of data indicate a relationship between the items of data. The selecting 104 is done based on, for example, items of the PVT data having composition properties or black oil properties that are physically related (see also description of
The method 100 further comprises the step of data clustering 106, using of at least one of a plurality of clustering schemes, the selected items of the PVT data into a plurality of clusters by a clustering module 304. The data clustering is done using, for example, a K-Means clustering scheme. A cluster of PVT data comprises items of data having similar properties or features. The K-Means clustering scheme is an iterative algorithm that partitions the items of data into distinct and non-overlapping clusters in which each item of data belongs to only a single cluster. The K-Means clustering scheme further comprises determining an arithmetic mean of the items of data in the clusters cluster. This arithmetic mean is called “centroid of the cluster”. The iterations of the K-Means clustering scheme are performed until a sum of squared distances of each of the items of data in the cluster is at a minimum. Other clustering schemes such as density-based spatial clustering of applications with noise, also referred to as “DBSCAN” or hierarchical clustering can also be used. The step of data clustering 106 is used to improve an overall performance of a prediction in the method 100.
The method 100 comprises the step of performing 107 machine learning by a machine learning module 305 on ones of the plurality of clusters to predict missing items of data for the black oil properties and compositional properties in the incomplete set of PVT data and thus to obtain a complete set of PVT data.
The incomplete set of the PVT data for hydrocarbon fluid samples comprises black oil properties and compositional properties. The black oil properties comprise at least one of reservoir temperature, solution gas-oil ratio, oil API gravity, gas gravity, dead oil viscosity, saturation pressure, saturated bubble point oil formation factor at saturation pressure, fluid density at reservoir conditions, fluid compressibility at reservoir conditions, viscosity at reservoir conditions, fluid density at reservoir conditions, and wherein the compositional properties comprise at least one of mole fractions of the components, in particular N2, H2S, CO2, C1, C2, C3, C4, C5, C7, C8, and pseudo-components, in particular C7+, C12+, C20+ and C36+ or any other pseudo-component as well as the molecular weight of the pseudo-components.
The data clustering step 106 of method 100 is applied prior to the step of performing 107 machine learning by a machine learning module 305. The data clustering step 106 is used to categorize the PVT data into families based on their collective behavior of the different features of the PVT data and, hence, improve the quality of the PVT samples properties prediction using machine learning.
The method 100 further comprises the step of performing 107 machine learning by the machine learning module 305. The method 100 further comprises the step of predicting 108 fluid properties for incomplete sets of PVT data. The incomplete sets of the PVT data comprise, for example, only a reduced number of items of data on the compositional properties or the pseudo-components (see above) of the hydrocarbons. These incomplete sets of PVT data are therefore missing some items of the data, for example on the properties or the pseudo-components of the hydrocarbons. The machine learning module 305 is used to predict these missing properties in the incomplete sets of PVT data. This predicting of the missing properties is used, for example, when a new PVT sample is acquired and when this newly acquired new PVT sample does not have a complete set of data and is missing items of data relating to the compositional properties or the pseudo-components. The method 100 comprises the step of plotting 108 the machine learning predictions by the machine learning module 305. The method 100 further comprises the step of comparing 109 the identified plurality of correlation results with the machine learning predictions.
The method 100 according to this first aspect uses algorithms to predict in series, starting from the incomplete set of PVT data (black oil/and compositional) to obtain a complete set of PVT data through method 100 that predicts and uses highest correlating properties first, wherein the highest correlating properties are the properties of the PVT data (black oil/and compositional) having the highest degree of correlation.
The method 100 further comprises that the step of the data clustering 106 further comprises the step of completing fluid composition including C12+, C20+ and C36+ mole fraction and molecular weight and/or completing black oil properties including, in the following order: solution gas oil ratio (GOR), BO@Psat, and saturation pressure (Psat). Thereby, it is possible to learn from existing data and “automatically” complete missing PVT data in every PVT sample using a step-by-step process. The order through which PVT data is completed is set out in the following order and relies on benefiting from the existing PVT data to predict missing PVT data starting from the highest correlating PVT data to the lowest correlating PVT data. The order through which the PVT data is completed allows a minimization of minimize propagation of error from prediction of PVT data in an earlier step of the process to a later step in the process. The following terminology is used:
= Npi − Npi
The output properties Propout are predicted using the most optimal machine learning method. In particular, as an Input: N_pi samples, N_po samples and Propout are used to predict for an output: Complete Propout for N_po samples.
The flowing algorithm is used to predict any missing property Propout:
MLerror_min=big_number
For every ML method
In one aspect of the present invention, the PVT data can be completed without the data clustering step 106, however this is not limiting the present invention. To complete composition data without the data clustering step 106, as an input all samples with compositional data can be used, in particular, mole fractions of N2, H2S, CO2, C1, C2, C3, C4, C5, C6 and C7+ as well as C7+MW. The output are samples with completed C12+, C20+ and C36+ mole fraction and molecular weight (for the specific cluster).
The flowing algorithm is used to complete composition data:
ML_optimal(Mole Fraction C12+)
To complete black oil data, as an input already completed C12+, C20+ and C36+ mole fractions and MW can be used. The output are samples with completed black oil properties.
The flowing algorithm is used to complete black oil data:
ML_optimal(GOR)
ML_optimal(BO_Psat)
ML_optimal(Psat)
In another aspect of the present invention, the PVT data can be completed with the data clustering step 106, however this is not limiting the present invention. To complete composition data with the data from the data clustering step 106, as an input all samples with compositional data can be used, in particular, mole fractions of N2, H2S, CO2, C1, C2, C3, C4, C5, C6 and C7+ as well as C7+MW. As an output, every sample will be associated with one of the clusters identified in the data clustering step 106.
A data completion algorithm is run for a plurality of clustering schemes to complete all missing compositional and black oil data in the clusters cluster. This data completion algorithm is also referred to as “chain algorithm”. The chain algorithm comprises the steps completing the compositional data for the cluster and subsequently completing the black oil data for the clusters cluster. The chain algorithm further comprises the step of calculating a cumulative root mean square error (RMSE), also referred to as “cumulative error”, for the clusters and then selecting the clustering scheme having the smallest cumulative error. The following is a description of the steps of the chain algorithm:
For every Clustering Scheme (CM)
In the chain algorithm, items of data generated in one step are used by a successive step. For instance, the mole fraction of C12+ is predicted using compositional properties up to C7+. Several machine learning (ML) algorithms are evaluated and the ML algorithm with the lowest cumulative error is selected. These ML algorithms are, for example, open source ML algorithms such as Scikit-learn. Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning and comprises a plurality of modules. The ML algorithms are, for example, linear regression algorithms, support vector regression algorithms, K neighbors regression algorithms, regression tree algorithms, extra tree algorithms, random forest algorithms, gradient boosting algorithms, multi-layer perceptron algorithms, bagging algorithms, or AdaBoost algorithms. The selected ML algorithm is then trained on all the complete data to predict the missing values pertaining to the mole fraction of C12+. At this stage, all samples have a complete mole fraction of C12+. This property is then used as input for the later steps. The molecular weight of C12+ using compositional properties up to C7+ along with the mole fraction of C12+ is then predicted. The following table shows the inputs and output of the chain algorithm.
In step 800, the items of data Npi are split into items of data used as training data Npi,train and items of data used as test data Npi,test. The training data is used for the training of hyperparameters of the ML algorithms. The hyperparameter is a parameter whose value is used to control a learning process of the ML algorithm. These hyperparameters are also referred to as “tuning parameters” for the ML algorithm. The test data is not used for the testing but kept until the training is finished. The test data is then used for a final evaluation of the ML algorithms. The test data is therefore used to quantify how the ML algorithms will perform on unclassified items of data. The objective of this testing is to select the ML algorithm with the least error on the unclassified items of data.
The input properties for the ML algorithm are then selected using the EDA in step 801. The input properties are selected by the EDA based on a degree of correlation between these input properties. A value for a minimal error is set as a big number, for example greater than 1,000. in step 802 and a value for m is set to m=1. Variable m is used as a counter and is, for example, an integer. After the selecting of the input properties, the hyperparameter of the ML algorithm are tuned in step 803. The tuning in step 803 of these hyperparameters is done automatically by the ML algorithm. In step 804 every ML algorithm is trained using the training data.
In step 805 the ML algorithm is tested using the test data Npi,test For example, a grid search approach with five folds of cross validation is used to select the parameters for every ML algorithm. In this validation, the training data is divided into five equal size subsets where each subset serves as validation data exactly once. For every possible combination of the hyperparameters, the ML algorithm is trained on four subsets of the training data and validated on the remaining one subset of the training data. This will result in five error values—one for every fold or subset—for every combination of the hyperparameters. An average of these five error values is then calculated in step 806. This average describes an error value for every set of hyperparameters. The hyperparameters with the least average error from the five folds or subset are then selected as parameters for the selected ML algorithm.
The ML algorithm is then evaluated on the unclassified items of the test data in step 807. Using the error values for the ML algorithm calculated using, for example, R2 and root mean square error (RMSE) methods, the ML algorithm is then evaluated. R2 describes a proportion of a variance in a response variable that can be explained by a predictor variable. R2 is used to evaluate numerical predictions an amount of variance explained by ML algorithm. The R2 is calculated using the following equation, where n is the number of items of data in the data set, yi is the value of the item of data i:
The value for μ is calculated using the following equation:
The RMSE is a metric for quantifying an average distance between predicted items of data from the ML algorithm and actual items of data in the data set.
Using the R2 or the RMSE, the ML algorithms are evaluated before the selecting in step 808 of the ML algorithm. This evaluation may also be done using, for example, linear regression, support vector regression, regression tree, Random Forest, gradient boosting, AdaBoost, Bagging, neural networks, and ensemble model. The selecting in step 808 of the ML algorithms is, for example, done by selecting the ML algorithm with a smallest value for the RMSE when applied to the test data. This selecting is performed for every output in every cluster. After the selecting of the ML algorithm, the counter m is incremented by 1 in step 809. In step 810 is conducted to verify if the counter m is equal to M, wherein M is a total number of ML algorithms used in this analysis. If m is not equal to M in step 810, steps 803 to 810 are reiterated. If m=Min step 810, the ML algorithm is retrained with all samples from the Npi in step 811. The missing properties Propout are then determined in step 812.
The PVT data set is parsed into a standard format and passed to later components in the data reader module 305. The step of data clustering 106 divides the PVT data from the PVT data set into groups such that samples in one cluster are likely to be similar. This unravels hidden insights and facilitates learning and improve performance Empirical correlations available in the literature can be applied to provide reference results (in terms of predictive capability) to the final machine learning results. The step of machine learning can be performed on the whole PVT data set at once. More importantly, machine learning can be performed on every one of the clusters to predict missing PVT properties and prepare for predicting PVT properties for incomplete PVT data. As can be seen in
The following correlations are processed in the present invention:
As can be seen in
The following list of machine learning can be processed with the present invention, however, the present invention is not limited thereby.
For achieving the expected result in the present invention, the following steps are proceeded. In the first step the code design is ingested, whereas the PVT data base 301 is read. The PVT data base 301 is structured for example as multiple excel files as follows: PVT Project: containing information about the project of which the fluid sample was taken such as the project's name, the year, the laboratory in which the experiment was done, the well from which the sample was taken, etc.; PVT Black Oil Properties: containing the black oil properties for each fluid sample such as the oil gravity API, reservoir temperature, pressure, bubble point pressure, oil formation volume factor at the bubble point pressure, etc.; Well Coordinates: containing entries of all the wells with the X and Y coordinates of each; and Compositions: containing the molecular composition for each fluid sample for the three sample types which are the “Reservoir Field”, “Evolved Gas”, and “Stock-Tank Oil”. The molecular composition of the heavy components is lumped.
In order to have the complete PVT data for each of the fluid samples, the PVT data from the different excel files are merged and linked to each other using the project ID which is unique for each project. Therefore, the PVT project and PVT Black oil properties are merged using the project ID as unique key for the sample. To add the X and Y coordinates to each fluid sample, each sample is linked to the Well Coordinates excel file with its well name. Then, the coordinates of that well are associated with the sample. The composition of each sample is also linked with previous data using the project ID. The mole fractions of C7+, C12+, C20+, and C36+ are calculated as follows: Mole fraction of C7+=mole fraction of M-C-05+ . . . + mole fraction of C36; Mole fraction of C12+=mole fraction of C12+ . . . + mole fraction of C36; Mole fraction of C20+=mole fraction of C20+ . . . + mole fraction of C36; Mole fraction of C36+=mole fraction of C36. However, heavy components are lumped, and the heavy component (Plus Fraction) is indicated in the PVT project excel file. Therefore, when the heavy component is reached, all other mole fractions should be zero. The molecular weights of C7+, C12+, C20+, and C36+ are also calculated by performing several intermediate calculations and using the previously calculated mole fractions.
In the second step data a screening and quality check (QC) is applied. The screening and quality check mechanism is applied to better understand the PVT data and to ensure that the PVT data does not contain any anomalies. The PVT data comprises data from the hydrocarbon fluid samples that are complete as well as hydrocarbon fluid samples that are missing values of the data, e.g., values being set to zero in the sample. The PVT data is therefore grouped into two categories. The first category in the PVT data are values for the hydrocarbon fluid samples that exist but are absent from the samples, such as Psat or GOR. The values of this first category can occur when items of data are “lumped”. These values are, for example, a C20+ mole fraction for a hydrocarbon liquid sample. This value must always be >0. The value might, however, be shown as =0 because of grouping that has been applied to the sample. The value might also be shown as =0 because the compositional analysis has only been performed up to C7+. The molecular weight for these absent heavy fractions is set to zero in the dataset. The second category of PVT data comprises values for the hydrocarbon fluid samples that are missing because the data does do not physically exist for these samples. For example, Bo@Psat does not exist for a hydrocarbon vapor sample.
This quality check step is done before the PVT data is used by the clustering schemes and machine learning algorithms. The PVT data identified with anomalies are flagged so that this data with the anomalies are not used in the clustering module 303 and machine learning modules 305. The steps of screening and QC comprises the following: Identifying the anomalies in the input PVT data i.e., unphysical values; Flagging of the PVT data and preparing data structure with proper indicators on complete and missing properties from each of the PVT data. This step is carried out before passing the information to the clustering schemes and machine learning modules. This enables the completion of the information once machine learning models are built.
The results of the screening and the QC of the PVT data base 301 are the following: The total number of PVT data is, for example, 1711. With respect to the black oil properties, 593 out of 568 samples have a complete black oil (BO) set of properties with regard to the black oil properties, as can be seen in
With respect to the dynamic data ingestion, every step uses the maximum number of samples. For example, when machine learning is used to predict Psat from “Composition” properties only, all the samples that have values for Psat and Composition should be used. These samples may not have Bo or other Black Oil properties. Accordingly, the step during which samples to be tagged complete/incomplete is a “dynamic” tag rather than a “static” tag. For machine learning, Clustering or EDA, this tagging step is performed once “Run” has been clicked. The general rules for samples with Mole Fraction C7+, C12+, C20+ or C36+=Zero are as follows: Mole Fraction of C7+, C12+, C20+ or C36+ are typically >0 for Hydrocarbon Liquid samples; Mole Fraction of C36+ is typically =0 for Hydrocarbon Vapor (gas) samples. Mole Fraction of C20+ is typically =0 for Hydrocarbon Vapor (gas) samples unless we have a gas condensate reservoir; Mole Fraction of C7+ is typically >0 for Hydrocarbon Vapor (gas) samples unless we have dry gas (predominantly methane); Molecular Weight is >>0. However, it should be “meaningless”, when the mole fraction=0. It should not be used in this case. Therefore, mole fraction of C36+ may be 0. It is a valid value (given the above). However, in this case, the molecular weight of C36+ for that specific sample should not be used and is considered undefined. This particular case exists in the case of EDA and Clustering. It does not exist in the case of machine learning. The following observations can be made when looking at the Psat, Bo, and mole fractions of C1, C7+, C12+, C20+ and C36+: Since Bo is an Oil property, it is unlikely to have samples (with defined Bo) with mole fraction of C7+, C12+, C20+ or C36+=0; For samples with defined Bo, the mole fraction of methane is typically <50% or 40%.
In the third step, data analysis is performed to generate statistics about the PVT data. This helps to better understand the distribution of the PVT data to be able to evaluate and interpret the PVT data. The following statistics and property distributions are automatically generated and reported using this framework: Distribution of the data across the fields; Distribution of the data across the reservoirs; Missing Values for each set of properties; Data distribution of the black oil properties, e.g., Saturation Pressure, API Gravity, Reservoir Temperature, Saturated bubble point oil formation volume factor, GOR, Density at saturation pressure, Viscosity at saturation pressure, Data distribution of Compositional Properties (Mole fraction of C7+, Mole fraction of C12+, Mole fraction of C20+, Mole fraction of C36+, Molecular weight of C7+, Molecular weight of C12+, Molecular weight of C20+, Molecular weight of C36+).
Examples of data statistics are presented in the
With respect to the data prediction, algorithms are used to predict, using machine learning methods, any PVT property (Black oil or compositional) from any set of properties (black oil or compositional). Any of the properties in FLUID PROPERTIES can be predicted as a function of a sub-set of all other properties. The algorithm is generic and allows for adding any other property that can be obtained in a laboratory analysis (e.g. CCE, CVF, DL, MSS) at any pressure.
For validation, the present method 100 runs first machine learning to predict Psat and Bo@Psat from the same parameters used in the literature to predict these two parameters with correlations: temperature, gas oil ratio, stock tank API gravity and gas gravity. For understanding dependencies and eliminating irrelevant features, exploratory data analysis (EDA) is used in the present invention to decrease the number of features used in PVT data prediction. The EDA explores correlation between parameters to, systematically, eliminate parameters with no correlation from the machine learning models. Example results are presented in
With respect to improved accuracy prediction using the data clustering step 106, the data clustering step 106 is used to categorize the PVT data into families based on their collective behavior of their different features. The data clustering step 106 is performed for two main reasons: Using machine learning on different clusters instead of the whole PVT data set with the purpose of improving the predictive capability of different methods for different clusters; the EoS models available for different clusters will be compared with each other. In case similarity is found between these EoS models, one representative EoS model is selected per cluster. The EoS models are thermodynamic models used for predicting the fluid properties under an expected range of pressure and a temperature covering a life of a reservoir. Once every sample belongs to a given cluster, the representative EoS model for that specific sample will be adopted for the specific sample.
The clustering 106 of method 100 can be performed using the following options: Clustering using black oil properties only; Clustering with compositional properties only; and Clustering with black oil and compositional properties. In all three cases, the clustering results can be used for machine learning to predict any of the black oil or compositional properties. In the following, the clustering to predict black oil properties is described to find out whether black oil properties can be predicted using compositional properties only without impacting the quality of the prediction.
For black oil clustering, the properties used for clustering are the following. Any other property could be used. Similarly, less properties or any other combination of properties can also be used.
Reservoir Temperature (Tres);
Solution gas oil ratio (GOR);
API Gravity;
Gas Gravity;
Saturation pressure (Psat);
Bo@Psat;
Viscosity @ Psat; and
Density @ Psat.
The number of samples used in this case is 593 samples. These are the samples with all above properties available.
For compositional properties based clustering, clustering is performed using completed compositional properties for which the compositional information is available for the full set of 1599 samples. This leads to a total of 1599 samples used in the clustering. Optimal number of clusters is this case is 5.
For black oil and compositional based clustering, clustering is performed using the following black oil properties:
Completed compositional properties for which the compositional information is available for the 593 samples. The total number of samples that have all above data is 593.
Further, clusters can be associated with fields and reservoirs. The association between clusters of PVT data identified through the clustering step oil and gas fields/reservoirs with potentially different thermodynamic behavior.
Further, the method 200 comprises the step of lumping 202 the PVT data from the complete set of PVT data into a pre-defined set of components and pre-defined set of pseudo-components to generate a plurality of equation of state (EoS) models. Further, the method 200 comprises generating 203 for the PVT samples on the PVT data set an EoS model using a same set of tuning parameters and thereby generating an EoS fluid model fingerprint for the hydrocarbon fluid samples. The EoS fluid model fingerprint refers to a specific heavy fraction characterization of the PVT data that defines a specific EoS model. The properties of the heavy fractions are then tuned or adjusted to match the laboratory data leading to unique characteristics of that specific fluid and its corresponding EoS model. Further, the method 200 comprises the step of associating 204 properties of the hydrocarbon fluid samples with the generated EoS fluid model fingerprint.
The method 200, as shown in
The step of associating 204 of method 200 further comprises the method step of clustering the selected items of the PVT data into a plurality of clusters for performing machine learning on each one of the plurality of clusters.
The method 200 further comprises the step of comparing 205 the results from clustering of the selected items of the PVT data with the plurality of equations of state (EoS) models and with heatmaps applying EoS models on the selected items of the PVT data.
The clustering 106, 204 of the selected items of the PVT data includes the step of identifying of clusters to which PVT data belong.
For delumping 201 into a common set of detailed components, as an input, the method step starts with SAMPLES that may or may not have the same set of DETAILED COMPOSTION or to a common set of components and pseudo-components. The method 100 according to the first aspect of the present invention is used to complete all SAMPLES into the same set of detailed components or to a common set of components and pseudo-components. The completing of the SAMPLES is done using the chain algorithm.
As a first example, some samples would have the following set of components and pseudo-components: CO2, H2S, N2, C1, C2, C3, iC4, nC4, iC5, nC5, C6, C7+. Mole fraction of C7, C8, . . . , C36+ and MW of C36+ will be predicted using ML models.
As a second example, some samples would have the following set of components and pseudo-components: CO2, H2S, N2, C1, C2, C3, iC4, nC4, iC5, nC5, C6, C7+. Mole fraction and MW of C12+, C20+ and C36+ will be predicted using ML models.
The step of lumping 202 into a predefined set of pseudo-components, in particular together with the step of delumping 201, are key to generate the multiple EoSs structured in the same format to enable building machine learning models. The step of delumping 201 refers to predicting a detailed composition of a sample. This delumping 201 is performed using the chain algorithm (see also description of
The following table shows an example for the delumping 201 and lumping 202. In the example shown, regression takes place on the lumped compositions with three pseud-components.
With respect to the step of generating 203 for the PVT samples on the PVT data set an EoS model using the same set of tuning parameters and thereby generating an EoS fluid model fingerprint for the hydrocarbon fluid samples, for each validated sample in the data base 301, an EoS model is automatically calibrated using the same set of tuning parameters. This results in a set of values of these tuning parameters, for each PVT sample, which will be considered as the EoS fluid model fingerprint for each sample. The following steps are provided:
Further, with respect to the step of associating 204 properties of the hydrocarbon fluid samples with the generated EoS fluid model fingerprint, an association between samples properties and EoS fluid model fingerprint is build. This associating step 204 consists of training machine learning models to predict the EoS fluid model fingerprint for each sample and, therefore, EoS Models for any new PVT sample with the highest accuracy (see also description of
The use of machine learning to build models to predict P1, P2, P3, . . . , PN based on the PVT samples properties (Compositional and Black oil) has the following input:
Further, the associating step 204 of associating properties of the hydrocarbon fluid samples with the generated EoS fluid model fingerprint can optionally include clustering. Accordingly, multiple machine learning models can be trained on the different clusters as described with the method 100 according to the first aspect of the invention. Several validation steps and insights will be drawn by comparing the trends from clustering of original samples, EoS models, and from heatmaps applying EoS models on all samples; all aiming towards improving the accuracy of the predicted EoS Model.
The method 200 according the third aspect of the invention can further include the step of validating. The step of validating can be applied to new samples or samples that have not been used in the machine learning step. The step of validation can be processed as following:
The system 400 for generating equations of state (EoS) for a plurality of hydrocarbon fluids comprises a first module 315, a second module 325, a third module 335 and a fourth module 345. The first module 315 delumps the pressure-volume-temperature (PVT) data for the hydrocarbon fluid samples from a complete set of the PVT data to one of a set of detailed fluid components, or to a common set of components and pseudo-components. The second module 325 lumps the PVT data for hydrocarbon fluid sample into a pre-defined set of components and pre-defined set of pseudo-components to generate a plurality of equation of state (EoS) models. The third module 335 generates for the hydrocarbon fluid samples in a PVT data base an EoS model using a same set of tuning parameters and thereby generating an EoS fluid model fingerprint for the hydrocarbon fluid samples. The fourth module 345 associates properties of the hydrocarbon fluid samples with the generated EoS fluid model fingerprint.
The workflow shown in
The ML algorithms are then used to predict to predict P1, P2, P3, . . . , PN based on the PVT sample properties (compositional properties and black oil properties). The inputs for the process are consists of compositional and black oil properties for all PVT samples. The results for the EoS tuning for the items in the PVT data are P1_Si, P2_Si, P3_Si, . . . , PN_Si. The output of the process is P1, P2, P3, . . . , PN. TheseTheseThis items of output data provide a full match between the laboratory results and the EoS results for the samples. The set of EoS tuning parameters resulting from the above steps is then validated and used by the EoS MODULE to model the PVT samples. The results are then compared with conventional regression-based results.
While only selected embodiments have been chosen to describe the present invention, it is apparent to the person skilled in the art from this disclosure that various changes and modifications can be made therein without deviating from the scope of the invention as defined in the attached claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 20191806.7 | Aug 2020 | EP | regional |
This application is a national phase of International Application No. PCT/IB2021/057475, filed Aug. 13, 2021, which claims priority to European Patent Application No. 20191806.7, filed Aug. 19, 2020, each of which is hereby incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IB2021/057475 | 8/13/2021 | WO |
