Machine Learning and Artificial Intelligence Provides Wolfcamp Completion Design Insight

2021 ◽  
Author(s):  
Robert Shelley ◽  
Oladapo Oduba ◽  
Howard Melcher
Keyword(s):  
Machine Learning ◽  
Water Saturation ◽  
Saturation Pressure ◽  
Ann Model ◽  
Permian Basin ◽  
Treatment Rate ◽  
Feed Forward Neural Network ◽  
Well Production ◽  
Reservoir Type ◽  
The Impact

Abstract The subject of this paper is the application of a unique machine learning approach to the evaluation of Wolfcamp B completions. A database consisting of Reservoir, Completion, Frac and Production information from 301 Multi-Fractured Horizontal Wolfcamp B Completions was assembled. These completions were from a 10-County area located in the Texas portion of the Permian Basin. Within this database there is a wide variation in completion design from many operators; lateral lengths ranging from a low of about 4,000 ft to a high of almost 15,000 ft, proppant intensities from 500 to 4,000 lb/ft and frac stage spacing from 59 to 769 ft. Two independent self-organizing data mappings (SOM) were performed; the first on completion and frac stage parameters, the second on reservoir and geology. Characteristics for wells assigned to each SOM bin were determined. These two mappings were then combined into a reservoir type vs completion type matrix. This type of approach is intended to remove systemactic errors in measuement, bias and inconsistencies in the database so that more realistic assessments about well performance can be made. Production for completion and reservoir type combinations were determined. As a final step, a feed forward neural network (ANN) model was developed from the mapped data. This model was used to estimate Wolfcamp B production and economics for completion and frac designs. In the performance of this project, it became apparent that the incorporation of reservoir data was essential to understanding the impact of completion and frac design on multi-fractured horizontal Wolfcamp B well production and economic performance. As we would expect, wells with the most permeability, higher pore pressure, effective porosity and lower water saturation have the greatest potential for hydrocarbon production. The most effective completion types have an optimum combination of proppant intensity, fluid intensity, treatment rate, frac stage spacing and perforation clustering. This paper will be of interest to anyone optimizing hydraulically fractured Wolfcamp B completion design or evaluating Permian Basin prospects. Also, of interest is the impact of reservoir and completion characteristics such as permeability, porosity, water saturation, pressure, offset well production, proppant intensity, fluid intensity, frac stage spacing and lateral length on well production and economics. The methodology used to evaluate the impact of reservoir and completion parameters for this Wolfcamp project is unique and novel. In addition, compared to other methodologies, it is low cost and fast. And though the focus of this paper is on the Wolfcamp B Formation in the Midland Basin, this approach and workflow can be applied to any formation in any Basin, provided sufficient data is available.

scholarly journals Prediction of Lap Shear Strength and Impact Peel Strength of Epoxy Adhesive by Machine Learning Approach

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 872
Author(s):  
Haisu Kang ◽  
Ji Hee Lee ◽  
Youngson Choe ◽  
Seung Geol Lee
Keyword(s):  
Machine Learning ◽  
Shear Strength ◽  
Linear Correlation ◽  
Peel Strength ◽  
Epoxy Adhesive ◽  
Data Sets ◽  
Ann Model ◽  
Lap Shear Strength ◽  
Lap Shear ◽  
The Impact

In this study, an artificial neural network (ANN), which is a machine learning (ML) method, is used to predict the adhesion strength of structural epoxy adhesives. The data sets were obtained by testing the lap shear strength at room temperature and the impact peel strength at −40 °C for specimens of various epoxy adhesive formulations. The linear correlation analysis showed that the content of the catalyst, flexibilizer, and the curing agent in the epoxy formulation exhibited the highest correlation with the lap shear strength. Using the analyzed data sets, we constructed an ANN model and optimized it with the selection set and training set divided from the data sets. The obtained root mean square error (RMSE) and R2 values confirmed that each model was a suitable predictive model. The change of the lap shear strength and impact peel strength was predicted according to the change in the content of components shown to have a high linear correlation with the lap shear strength and the impact peel strength. Consequently, the contents of the formulation components that resulted in the optimum adhesive strength of epoxy were obtained by our prediction model.

scholarly journals Detection of damaged seeds in laboratory evaluation of precision planter using impact acoustics and artificial neural networks

2012 ◽  
Vol 1 (2) ◽  
pp. 67 ◽  
Author(s):  
Hadi Karimi ◽  
Hossein Navid ◽  
Asghar Mahmoudi
Keyword(s):  
Neural Network ◽  
Intelligent System ◽  
Acoustic Signals ◽  
Laboratory Evaluation ◽  
Ann Model ◽  
Feed Forward Neural Network ◽  
Ann Models ◽  
Artificial Neural ◽  
Seed Metering Device ◽  
The Impact

In the present study, feasibility of laboratory detection of damaged seeds in precision planters caused by malfunction of seed metering device was investigated. An acoustic-based intelligent system was developed for detection of damaged pelleted tomato seeds. To improve the Artificial Neural Network (ANN) models a total of 2000 seeds sound signals, 1000 samples for damaged seeds and 1000 for undamaged ones were recorded. When seed metering device drove out seeds, the ejected seeds were impacted to steel plate, and their acoustic signals were recorded from the impact. The bounced seeds lied on the running grease belt. In each stage of experiments, damaged seeds were determined manually in grease belt and related damaged seed sound signals were designated. Achieved acoustic signals, were processed and potential features were extracted from the analysis of sound signals in time and frequency domains. The method is based on feature generation by Fast Fourier Transform (FFT), feature selection by statistical methods and classification by Multilayer Feed forward Neural Network. Features such as amplitude, phase and power spectrum of sound signals were computed through a 1024-point FFT. By using statistical factors (maximum, minimum, median, mean and variance) for each vector of data, feature vector was reduced to 15 factors. In developing the ANN models, several ANN architectures, each having different numbers of neurons in hidden layer, were evaluated. The best model was chosen after a number of evaluations based on minimizing the mean square error (MSE), correct detection rate (CDR) and correlation coefficient (r). Selected ANN, 15-17-2 was configured for classification. CDR of the proposed ANN model for undamaged and damaged seeds was 99.49 and 100 respectively. MSE of the system was found to be 0.0109.

scholarly journals Ambient temperature change Vs confirmed cases of COVID-19: A machine learning model

2020 ◽  
Author(s):  
Amir Jamshidnezhad ◽  
Seyed Ahmad Hosseini ◽  
Leila Ibrahimi Ghavamabadi ◽  
Marzieh Zilae ◽  
Hediye Mousavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climatic Factors ◽  
Linear Regression Analysis ◽  
Ambient Air ◽  
Multiple Linear Regression Analysis ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Testing Stage ◽  
Artificial Neural Network Ann ◽  
The Impact

Abstract Background The relation between ambient air temperature and prevalence of viral infection has been under investigation in recent years. The present study aimed at providing the statistical and machine learning based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran.Method The data of confirmed cases of COVID-19 as well as some climatic factors related to 31 provinces of Iran, during 04/03/2020 to 05/05/2020 were gathered from the official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of Covid-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. Moreover, the statistical analysis were used to assess the trend of positive cases in comparison with the fluctuations of some climatic factors. Results The proposed ANN model showed the accuracy rate of 87.25% and 86.4% in the training and testing stage, respectively for classification of COVID-19 confirmed cases. Moreover, multiple linear regression analysis was obtained the R2 equal to 0.40 and 0.68 in two cities of Qom and Ahvaz, respectively. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. Conclusion This study clearly showed that with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable; thus the number of positive cases of COVID-19 increases. Also, this study shows the role of closed air cycle condition in indoor environment of tropical cities, along with the impact of climatic factors in the frequency of positive cases of COVID-19 and the capacity of ANN classification in the surveys.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

PODC 2020 Review

2021 ◽  
Vol 51 (4) ◽  
pp. 75-81
Author(s):  
Ahad Mirza Baig ◽  
Alkida Balliu ◽  
Peter Davies ◽  
Michal Dory
Keyword(s):  
Machine Learning ◽  
Distributed Computing ◽  
Keynote Speaker ◽  
Lively Discussion ◽  
Theoretical Understanding ◽  
New Directions ◽  
New Ideas ◽  
New Challenges ◽  
The Impact ◽  
Distributed Machine Learning

Rachid Guerraoui was the rst keynote speaker, and he got things o to a great start by discussing the broad relevance of the research done in our community relative to both industry and academia. He rst argued that, in some sense, the fact that distributed computing is so pervasive nowadays could end up sti ing progress in our community by inducing people to work on marginal problems, and becoming isolated. His rst suggestion was to try to understand and incorporate new ideas coming from applied elds into our research, and argued that this has been historically very successful. He illustrated this point via the distributed payment problem, which appears in the context of blockchains, in particular Bitcoin, but then turned out to be very theoretically interesting; furthermore, the theoretical understanding of the problem inspired new practical protocols. He then went further to discuss new directions in distributed computing, such as the COVID tracing problem, and new challenges in Byzantine-resilient distributed machine learning. Another source of innovation Rachid suggested was hardware innovations, which he illustrated with work studying the impact of RDMA-based primitives on fundamental problems in distributed computing. The talk concluded with a very lively discussion.

Sign in / Sign up

Export Citation Format

Share Document