Well GOR Prediction from Surface Gas Composition in Shale Reservoirs

Abstract There are increased development activities in shale reservoirs with ultra-low permeability thanks to the advances in drilling and fracking technology. However, representative reservoir fluid samples are still difficult to acquire. The challenge leads to limited reservoir fluid data and large uncertainties for shale play evaluation, field development, and production optimization. In this work, we built a large unconventional reservoir fluid database with more than 2400 samples from shale reservoirs in Canada, Argentina, and the USA, comprising early production surface gas data and traditional PVT data from selected shale assets. A machine learning approach was applied to the database to predict gas to oil ratio (GOR) in shale reservoirs. To enhance regional correlations and obtain a more accurate GOR prediction, we developed a machine learning model focused on Canada shale plays data, intended for wells with limited reservoir fluid data available and located within the same region. Both surface gas compositional data and well location and are input features to this model. In addition, we developed an additional machine learning model for the objective of a generic GOR prediction model without shale dependency. The database includes Canada shale data and Argentina and USA shale data. The GOR predictions obtained from both models are good. The machine learning model circumscribed to the Canada shale reservoirs has a mean percentage error (MAPE) of 4.31. In contrast, the generic machine learning model, which includes additional data from Argentina and USA shale assets, has a MAPE of 4.86. The better accuracy of the circumscribed Canada model is due to the introduction of the geospatial well location to the model features. This study confirms that early production surface gas data can be used to predict well GOR in shale reservoirs, providing an economical alternative for the sampling challenges during early field development. Furthermore, the GOR prediction offers access to a complete set of reservoir fluid properties which assists the decision-making process for shale play evaluation, completion concept selection, and production optimization.

Download Full-text

Mud-Gas Breakthrough Equinor Develops Real-Time Reservoir-Fluid Identification

Journal of Petroleum Technology ◽

10.2118/0221-0037-jpt ◽

2021 ◽

Vol 73 (02) ◽

pp. 37-39

Author(s):

Trent Jacobs

Keyword(s):

Machine Learning ◽

Real Time ◽

Oil And Gas ◽

Partial Information ◽

Learning Model ◽

Identification System ◽

Fluid Identification ◽

Reservoir Fluid ◽

Machine Learning Model ◽

The Difference

For all that logging-while-drilling has provided since its wide-spread adoption in the 1980s, there is one thing on the industry’s wish list that it could never offer: an accurate way to tell the difference between oil and gas. A new technology created by petrotechnicals at Equinor, however, has made this possible. The innovation could be thought of as a pseudo-log, but Equinor is describing it as a reservoir-fluid-identification system. Using an internally developed machine-learning model, it compares a database of more than 4,000 reservoir samples against the real-time analysis of the mud gas that flows up a well as it is drilled. Crunched out of the technology’s various hardware and software components is a prediction on the gas/oil ratio (GOR) that the rock being drilled through will have once it is producing. Since this happens in real time, it boils down to an alert system for when drillers are tapping into uneconomic pay zones. “This is something people have tried to do for 30 years - using partial information to predict entire oil and gas properties,” said Tao Yang. He added that “the data acquisition is rather cheap compared with all the downhole tools, and it doesn’t cost you rig time,” highlighting that the mud-gas analyzer critical to the process sits on a rig or platform without interfering with drilling operations. Yang is a reservoir technology specialist at Equinor and one of the authors of a technical paper (SPE 201323) about the new digital technology that was presented at the SPE Annual Technical Conference and Exhibition in October. He and his colleagues spent more than 3 years building the system which began in the Norwegian oil company’s Houston office as a project to improve pressure/volume/temperature (PVT) analysis in tight-oil wells in North America. It has since found a home in the company’s much larger offshore business unit in Stavanger. Offshore projects designed around certain oil-production targets can face harsh realities when they end up producing more associated gas than expected. It is the difference between drilling an underperforming well full of headaches and one that will pay out hundreds of millions of dollars over its lifetime. By introducing real-time fluid identification, Equinor is trying to enforce a new control on that risk by giving drillers the information they need to pull the bit back and start drilling a side-track deeper into the formation where the odds are better of finding higher proportions of oil or condensates. At the conference, Yang shared details about some of the first field implementations, saying that in most cases the GOR predictions made by the fluid-identification system were confirmed by traditional PVT analysis from the trial wells. Unlike other advancements made on this front, he also said the new approach is the first of its kind to combine such a large database of PVT data with a machine-learning model “that is common to any well.” That means “we do not need to know where this well is located” to make a GOR prediction, said Yang.

Download Full-text

Design of Machine Learning Model for Urban Planning and Management Improvement

International Journal of Performability Engineering ◽

10.23940/ijpe.20.06.p14.958967 ◽

2020 ◽

Vol 16 (6) ◽

pp. 958 ◽

Cited By ~ 1

Author(s):

Zhou Jiafeng ◽

Liu Tian ◽

Zou Lin

Keyword(s):

Machine Learning ◽

Urban Planning ◽

Learning Model ◽

Planning And Management ◽

Machine Learning Model ◽

Urban Planning And Management ◽

Management Improvement

Download Full-text

A Novel Machine Learning Model for Early Operational Anomaly Detection Using LWD/MWD Data

10.2523/iptc-19230-ms ◽

2019 ◽

Author(s):

Mohammed Al-Ghazal ◽

Viranchi Vedpathak

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Model ◽

Machine Learning Model

Download Full-text

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

10.26434/chemrxiv.7411172 ◽

2018 ◽

Author(s):

Steen Lysgaard ◽

Paul C. Jennings ◽

Jens Strabo Hummelshøj ◽

Thomas Bligaard ◽

Tejs Vegge

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Genetic Algorithms ◽

Au Nanoparticles ◽

Learning Model ◽

Energy Calculations ◽

Atomic Distribution ◽

Machine Learning Model ◽

Fold Reduction ◽

Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

Download Full-text

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200325104317 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dhilsath Fathima.M ◽

S. Justin Samuel ◽

R. Hari Haran

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Decision Tree ◽

Learning Model ◽

Training Dataset ◽

Data Set ◽

Machine Learning Model ◽

Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Download Full-text