scholarly journals Prediction of Hydraulic Fractured Well Performance Using Empirical Correlation and Machine Learning

2021 ◽  
Vol 44 (2) ◽  
pp. 131-145
Author(s):  
Kamal Hamzah ◽  
Amega Yasutra ◽  
Dedy Irawan
Keyword(s):  
Machine Learning ◽  
Hydraulic Fracturing ◽  
Petroleum Industry ◽  
Prediction Method ◽  
Empirical Correlation ◽  
Well Performance ◽  
Data Limitations ◽  
Medium Permeability ◽  
Water Cut ◽  
Fractured Well

Hydraulic fracturing has been established as one of production enhancement methods in the petroleum industry. This method is proven to increase productivity and reserves in low permeability reservoirs, while in medium permeability, it accelerates production without affecting well reserves. However, production result looks scattered and appears to have no direct correlation to individual parameters. It also tend to have a decreasing trend, hence the success ratio needs to be increased. Hydraulic fracturing in the South Sumatra area has been implemented since 2002 and there is plenty of data that can be analyzed to resolve the relationship between actual production with reservoir parameters and fracturing treatment. Empirical correlation approach and machine learning (ML) methods are both used to evaluate this relationship. Concept of Darcy's equation is utilized as basis for the empirical correlation on the actual data. The ML method is then applied to provide better predictions both for production rate and water cut. This method has also been developed to solve data limitations so that the prediction method can be used for all wells. Empirical correlation can gives an R2 of 0.67, while ML can gives a better R2 that is close to 0.80. Furthermore, this prediction method can be used for well candidate selection means.

Prediction of Hydraulic Fractured Well Performance Using Empirical Correlation and Machine Learning

2021 ◽  
Vol 44 (2) ◽  
pp. 141-152
Author(s):  
Kamal Hamzah ◽  
Amega Yasutra ◽  
Dedy Irawan
Keyword(s):  
Machine Learning ◽  
Hydraulic Fracturing ◽  
Petroleum Industry ◽  
Prediction Method ◽  
Empirical Correlation ◽  
Well Performance ◽  
Data Limitations ◽  
Medium Permeability ◽  
Water Cut ◽  
Fractured Well

Hydraulic fracturing has been established as one of production enhancement methods in the petroleum industry. This method is proven to increase productivity and reserves in low permeability reservoirs, while in medium permeability, it accelerates production without affecting well reserves. However, production result looks scattered and appears to have no direct correlation to individual parameters. It also tend to have a decreasing trend, hence the success ratio needs to be increased. Hydraulic fracturing in the South Sumatra area has been implemented since 2002 and there is plenty of data that can be analyzed to resolve the relationship between actual production with reservoir parameters and fracturing treatment. Empirical correlation approach and machine learning (ML) methods are both used to evaluate this relationship. Concept of Darcy's equation is utilized as basis for the empirical correlation on the actual data. The ML method is then applied to provide better predictions both for production rate and water cut. This method has also been developed to solve data limitations so that the prediction method can be used for all wells. Empirical correlation can gives an R2 of 0.67, while ML can gives a better R2 that is close to 0.80. Furthermore, this prediction method can be used for well candidate selection means.

A Comprehensive Machine Learning Approach for Quantitatively Analyzing Development Performance and Optimization for a Heterogeneous Carbonate Reservoir in Middle East

2021 ◽  
Author(s):  
Ruijie Huang ◽  
Chenji Wei ◽  
Baohua Wang ◽  
Baozhu Li ◽  
Jian Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component ◽  
Optimal Number ◽  
Gas Production ◽  
Carbonate Reservoir ◽  
Multidimensional Data ◽  
Well Performance ◽  
Reference Case ◽  
Evaluation Approach ◽  
Water Cut

Abstract Compared with conventional reservoir, the development efficiency of the carbonate reservoir is lower, because of the strong heterogeneity and complicated reservoir structure. How to accurately and quantitatively analyze development performance is critical to understand challenges faced, and to propose optimization plans to improve recovery. In the study, we develop a workflow to evaluate similarities and difference of well performance based on Machine Learning methods. A comprehensive Machine Learning evaluation approach for well performance is established by utilizing Principal Component Analysis (PCA) in combination with K-Means clustering. The multidimensional dataset used for analysis consists of over 15 years dynamic surveillance data of producers and static geology parameters of formation, such as oil/water/gas production, GOR, water cut (WC), porosity, permeability, thickness, and depth. This approach divides multidimensional data into several clusters by PCA and K-Means, and quantitatively evaluate the well performance based on clustering results. The approach is successfully developed to visualize (dis)similarities among dynamic and static data of heterogeneous carbonate reservoir, the optimal number of clusters of 27-dimension data is 4. This method provides a systematic framework for visually and quantitatively analyzing and evaluating the development performance of production wells. Reservoir engineers can efficiently propose targeted optimization measures based on the analysis results. This paper offers a reference case for well performance clustering and quantitative analysis and proposing optimization plans that will help engineers make better decision in similar situation.

Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori
Keyword(s):  
Machine Learning ◽  
Antidepressant Drug ◽  
Prediction Method ◽  
Analytical Framework ◽  
Structured Data ◽  
Withdrawal Symptoms ◽  
Machine Learning Techniques ◽  
Drug Discontinuation ◽  
Factors Associated ◽  
Learning Techniques

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

scholarly journals IIMLP: integrated information-entropy-based method for LncRNA prediction

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Junyi Li ◽  
Huinian Li ◽  
Xiao Ye ◽  
Li Zhang ◽  
Qingzhe Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Information Entropy ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Reading Frame ◽  
Non Coding Rna ◽  
The One ◽  
Long Non Coding Rna

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

Literary work education model based on intelligent machine learning and reader scoring criteria factors

2021 ◽  
pp. 1-10
Author(s):  
Lei Han ◽  
Wei Li ◽  
Ming Zang
Keyword(s):  
Machine Learning ◽  
Prediction Method ◽  
Literary Work ◽  
Time Decay ◽  
Recommendation Algorithm ◽  
Intelligent Machine ◽  
Literary Education ◽  
Education Model ◽  
Work Education ◽  
Rating Criteria

In order to improve the effect of literary works education, this paper combines intelligent machine learning and reader scoring criteria factors to construct an intelligent education model, and proposes a collaborative filtering recommendation algorithm based on item proportion factors and time decay. When calculating the user similarity, this paper adds the scale factor of the intersection of common scoring items to all the scoring items, and considers the non-intersection part of the user scoring items. Secondly, when predicting the project score, this paper adds a time decay function, combines the forgetting curve law to modify the score prediction method, and combines the actual needs to construct the basic framework of the education model. In addition, this paper designs experiments to verify the performance of the literary work education model constructed in this paper. The research results show that the literary work education model constructed in this paper based on intelligent machine learning and reader rating criteria factors has a certain role in promoting the effect of literary education.

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