Chain-based machine learning for full PVT data prediction

2021 ◽  
pp. 109658
Author(s):  
Kassem Ghorayeb ◽  
Arwa Ahmed Mawlod ◽  
Alaa Maarouf ◽  
Qazi Sami ◽  
Nour El Droubi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pvt Data ◽  
Data Prediction

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

A Comparative Analysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machine Learning Algorithms

2021 ◽  
Author(s):  
Thitaree Lertliangchai ◽  
Birol Dindoruk ◽  
Ligang Lu ◽  
Xi Yang
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Compositional Data ◽  
Machine Learning Algorithms ◽  
Empirical Correlation ◽  
Dew Point ◽  
Pvt Data ◽  
Point Pressure ◽  
Input Variables ◽  
Dew Point Pressure

Abstract Dew point pressure (DPP) is a key variable that may be needed to predict the condensate to gas ratio behavior of a reservoir along with some production/completion related issues and calibrate/constrain the EOS models for integrated modeling. However, DPP is a challenging property in terms of its predictability. Recognizing the complexities, we present a state-of-the-art method for DPP prediction using advanced machine learning (ML) techniques. We compare the outcomes of our methodology with that of published empirical correlation-based approaches on two datasets with small sizes and different inputs. Our ML method noticeably outperforms the correlation-based predictors while also showing its flexibility and robustness even with small training datasets provided various classes of fluids are represented within the datasets. We have collected the condensate PVT data from public domain resources and GeoMark RFDBASE containing dew point pressure (the target variable), and the compositional data (mole percentage of each component), temperature, molecular weight (MW), MW and specific gravity (SG) of heptane plus as input variables. Using domain knowledge, before embarking the study, we have extensively checked the measurement quality and the outcomes using statistical techniques. We then apply advanced ML techniques to train predictive models with cross-validation to avoid overfitting the models to the small datasets. We compare our models against the best published DDP predictors with empirical correlation-based techniques. For fair comparisons, the correlation-based predictors are also trained using the underlying datasets. In order to improve the outcomes and using the generalized input data, pseudo-critical properties and artificial proxy features are also employed.

scholarly journals Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Fermentation ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 104
Author(s):  
Claudia Gonzalez Viejo ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Electronic Nose ◽  
Near Infrared ◽  
Raw Materials ◽  
Low Cost ◽  
Consumer Preference ◽  
Sensor Technology ◽  
Type Model ◽  
Data Prediction

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.

scholarly journals CDL4CDRP: A Collaborative Deep Learning Approach for Clinical Decision and Risk Prediction

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 265 ◽  
Author(s):  
Mingrui Sun ◽  
Tengfei Min ◽  
Tianyi Zang ◽  
Yadong Wang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Missing Data ◽  
Clinical Data ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Personalized Recommendation ◽  
Data Prediction ◽  
Latent Features ◽  
Missing Data Prediction

(1) Background: Recommendation algorithms have played a vital role in the prediction of personalized recommendation for clinical decision support systems (CDSSs). Machine learning methods are powerful tools for disease diagnosis. Unfortunately, they must deal with missing data, as this will result in data error and limit the potential patterns and features associated with obtaining a clinical decision; (2) Methods: Recent years, collaborative filtering (CF) have proven to be a valuable means of coping with missing data prediction. In order to address the challenge of missing data prediction and latent feature extraction, neighbor-based and latent features-based CF methods are presented for clinical disease diagnosis. The novel discriminative restricted Boltzmann machine (DRBM) model is proposed to extract the latent features, where the deep learning technique is adopted to analyze the clinical data; (3) Results: Proposed methods were compared to machine learning models, using two different publicly available clinical datasets, which has various types of inputs and different quantity of missing. We also evaluated the performance of our algorithm, using clinical datasets that were missing at random (MAR), which were missing at various degrees; and (4) Conclusions: The experimental results demonstrate that DRBM can effectively capture the latent features of real clinical data and exhibits excellent performance for predicting missing values and result classification.

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