Artificial Intelligence Model for Predicting Formation Damage in Oil and Gas Wells

2021 ◽  
Author(s):  
Augustine James Effiong ◽  
Joseph Okon Etim ◽  
Anietie Ndarake Okon
Keyword(s):  
Mean Square Error ◽  
Goodness Of Fit ◽  
Oil And Gas ◽  
Performance Metrics ◽  
Petroleum Industry ◽  
Formation Damage ◽  
Mean Square ◽  
Ann Model ◽  
Gas Drilling ◽  
Well Completion

Abstract An artificial neural network (ANN) was developed to predict skin, a formation damage parameter in oil and gas drilling, well completion and production operations. Four performance metrics: goodness of fit (R2), mean square error (MSE), root mean square error (RMSE), average absolute percentage relative error (AAPRE), was used to check the performance of the developed model. The results obtained indicate that the model had an overall MSE of 355.343, RMSE of 18.850, AAPRE of 4.090 and an R2 of 0.9978. All the predictions agreed with the measured result. The generalization capacity of the developed ANN model was assessed using 500 randomly generated datasets that were not part of the model training process. The results obtained indicate that the developed model predicted 97% of these new datasets with an MSE of 375.021, RMSE of 19.370, AAPRE of 6.090 and R2 of 0.9731, while Standing (1970) equation resulted in R2of −0.807, MSE of 9.34×1016, AAPRE of 3.10×106 and RMSE of 4.10×105. The relative importance analysis of the model input parameters showed that the flow rates (q), permeability (k), porosity (φ) and pressure drop (Δp) had a significant impact on the skin (S) values estimated from the downhole. Thus, the developed model if embedded in a downhole (sensing) tool that capture these basic or required reservoir parameters: pressure, flowrate, permeability, viscosity, and thickness, would eliminate the diagnostic approach of estimating skin factor in the petroleum industry.

scholarly journals Flood Risk Prediction for a Hydropower System using Artificial Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 6177-6181
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Water Flow ◽  
Performance Metrics ◽  
Water Discharge ◽  
Mean Square ◽  
Ann Model ◽  
Elman Neural Network ◽  
Artificial Neural

Hydropower scheme would experience issue relating to high flooding especially at low lying area due to extreme raining season. To mitigate the potential risk of flooding and improve the hydroelectric regulation, a flow prediction is needed to estimate the discharge of water flow at hydroelectric reservoirs. Artificial Neural Network (ANN) model were used in this research to forecast the water discharge of hydroelectric station. The discharge flow predictions were made based on fore bay elevation, inflow and the discharge of water flow. Elman Neural Network architecture was selected as ANN method and its performance was evaluated by considering the number of hidden nodes and training methods. ANN model performance were assessed using performance metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE) and Sum Square Error (SSE). The result indicate that ANN model showed the best applicability for discharge prediction with small performance metric.

Time Series Based House Sale Value Market Forecasting Using Genetically Evolved Neural Networks

2010 ◽  
pp. 265-282
Author(s):  
George S. Atsalakis ◽  
Kimon P. Valavanis ◽  
Constantin Zopounidis ◽  
Dimitris Nezis
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Mean Square Error ◽  
Fuzzy Inference ◽  
Housing Prices ◽  
Individual Investors ◽  
Mean Square ◽  
Ann Model ◽  
Neuro Fuzzy ◽  
Proposed Model

Accurate forecasting of the house sale value market is important for individual investors, business investors, banks and mortgage companies. This chapter uses fundamentals of Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) to derive and implement a hybrid, genetically evolved feedforward ANN model that predicts next month house sale prices. Derived model results are compared with results obtained using a linear regression model and an Adaptive Neuro Fuzzy Inference System (ANFIS). The proposed model returned lower Root Mean Square Error (RMSE), Absolute Mean Error (MAE), Mean Square Error (MSE) and Mean Absolute Percent Error (MAPE) results compared with the linear regression and ANFIS models. For case studies real monthly data of USA housing prices from 1963 to 2007 were used.

scholarly journals Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 43 ◽  
Author(s):  
Dariusz Młyński ◽  
Andrzej Wałęga ◽  
Andrea Petroselli ◽  
Flavia Tauro ◽  
Marta Cebulska
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Daily Precipitation ◽  
Probability Distributions ◽  
Random Variables ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Probability Of Exceedance

The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (Pmaxp%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R2) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971–2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of Pmaxp% was determined based on the following probability distributions of random variables: Pearson’s type III (PIII), Weibull’s (W), log-normal, generalized extreme value (GEV), and Gumbel’s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.

Modeling of Phenol Degradation in Spouted Bed Contactor Using Artificial Neural Network (ANN)

2008 ◽  
Vol 3 (2) ◽  
Author(s):  
Madhukar A. Dabhade ◽  
M. B. Saidutta ◽  
D. V. R. Murthy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Model Prediction ◽  
Phenol Degradation ◽  
Mean Square ◽  
Ann Model ◽  
Spouted Bed ◽  
The Mean ◽  
Artificial Neural Network Ann

Presence of phenol and phenolic compounds in various wastewaters and its harmful effects has led to the use of different treatment methods. Work on biological methods shows the use of different microorganisms and different bioreactors so as to improve the removal efficiency economically. The present work deals with the use of N. hydrocarbonoxydans (NCIM 2386), an actinomycetes, for the degradation of phenol. N. hydrocarbonoxydans was immobilized on GAC and used in a spouted bed contactor for effective contact of microorganisms and the substrate. The contactor performance was studied by varying flow rates, influent concentrations and the solids loading in the contactor. The effect of these variables on phenol degradation was investigated and modeling study was carried out using the artificial neural network (ANN). A feed forward neural network with back propagation was used for the model development. The experiments were planned as per the face centered cube design (FCCD) and used for training of the model, whereas data from four other experimental runs were used for testing and validation of the model. The network was optimized for the number of neurons based on the mean square error. The ANN model with three layers with three input neurons, eight neurons in hidden layers and one output neuron was found to predict effectively the effluent concentration for the given operating conditions in the spouted bed contactor. The mean square error was found to be 9.318e-12 for this ANN model. Also the experimental data was used to develop second order nonlinear empirical model obtained using multiple regression (MR) and the results compared with ANN using correlation coefficient (R2), average absolute error (AAE) and root mean square error (RMSE). Results show that R2, AAE and RMSE values of MR model were 0.9363, 2.085 % and 2.338 % respectively, while in case of ANN model these values were 0.9995, 0.59 % and 1.263 % respectively. This shows that ANN model prediction is better than multiple regression model prediction.

scholarly journals Adaptação transcultural Portugal-Brasil do Inventário de Burnout de Maslach para estudantes

2012 ◽  
Vol 46 (5) ◽  
pp. 816-824 ◽  
Author(s):  
Juliana Alvares Duarte Bonini Campos ◽  
João Maroco
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Mean Square ◽  
Fit Index

OBJETIVO: Realizar a adaptação transcultural da versão em português do Inventário de Burnout de Maslach para estudantes e investigar sua confiabilidade, validade e invariância transcultural. MÉTODOS: A validação de face envolveu participação de equipe multidisciplinar. Foi realizada validação de conteúdo. A versão em português foi preenchida em 2009, pela internet, por 958 estudantes universitários brasileiros e 556 portugueses da zona urbana. Realizou-se análise fatorial confirmatória utilizando-se como índices de ajustamento o χ²/df, o comparative fit index (CFI), goodness of fit index (GFI) e o root mean square error of approximation (RMSEA). Para verificação da estabilidade da solução fatorial conforme a versão original em inglês, realizou-se validação cruzada em 2/3 da amostra total e replicada no 1/3 restante. A validade convergente foi estimada pela variância extraída média e confiabilidade composta. Avaliou-se a validade discriminante e a consistência interna foi estimada pelo coeficiente alfa de Cronbach. A validade concorrente foi estimada por análise correlacional da versão em português e dos escores médios do Inventário de Burnout de Copenhague; a divergente foi comparada à Escala de Depressão de Beck. Foi avaliada a invariância do modelo entre a amostra brasileira e a portuguesa. RESULTADOS: O modelo trifatorial de Exaustão, Descrença e Eficácia apresentou ajustamento adequado (χ²/df = 8,498; CFI = 0,916; GFI = 0,902; RMSEA = 0,086). A estrutura fatorial foi estável (λ: χ²dif = 11,383, p = 0,50; Cov: χ²dif = 6,479, p = 0,372; Resíduos: χ²dif = 21,514, p = 0,121). Observou-se adequada validade convergente (VEM = 0,45;0,64, CC = 0,82;0,88), discriminante (ρ² = 0,06;0,33) e consistência interna (α = 0,83;0,88). A validade concorrente da versão em português com o Inventário de Copenhague foi adequada (r = 0,21;0,74). A avaliação da validade divergente do instrumento foi prejudicada pela aproximação do conceito teórico das dimensões Exaustão e Descrença da versão em português com a Escala de Beck. Não se observou invariância do instrumento entre as amostras brasileiras e portuguesas (λ:χ²dif = 84,768, p < 0,001; Cov: χ²dif = 129,206, p < 0,001; Resíduos: χ²dif = 518,760, p < 0,001). CONCLUSÕES: A versão em português do Inventário de Burnout de Maslach para estudantes apresentou adequada confiabilidade e validade, mas sua estrutura fatorial não foi invariante entre os países, apontando ausência de estabilidade transcultural.

scholarly journals Completion Fluid dengan Nitrat - Formate Base

2020 ◽  
Vol 1 (2) ◽  
pp. 41-46
Author(s):  
Mohammad Mahlil Nasution
Keyword(s):  
Oil And Gas ◽  
High Pressures ◽  
Solid Material ◽  
Oil And Gas Industry ◽  
Basic Material ◽  
Formation Damage ◽  
Gas Industry ◽  
Well Completion ◽  
Soluble Solid ◽  
The Impact

Completion Fluid is a Liquid of Salt Solution (Brine), used during the Well Completion, also to Killing Well job, when doing Work Over Wells and Well Services Jobs and Fishing Job  and also functions as Packer Fluid. Completion Fluid is generally used in Reservoir formations that are sensitive to Shales, Clay or other minerals. The purpose of using Completion Fluid is to avoid or reduce formation demage. The formation damage causes reservoir formation that has hydrocarbon potential, after being drilled and produced the flow of oil becomes small and even difficult to flow. Formation damage  need to be given very serious attention so that the Oil production in our country can increase significantly because the impact is that production does not increase significantly, Cost of production is high. If an effort to minimize damage is done optimally, it is expected that production will increase significantly so that the production target from year to year can be achieved. This invention relates to the method of making Completion Fluid for Drilling, Work Over and Well Services as Drilling activities in the Oil and Gas industry, using fresh water and solids as the material, more specifically is a solid which is a soluble solid as a base formula for making the fluid. In this case, the basic material of the solid material used for completion fluid is Nitrate and Alkali Formate. This completion fluid can reach SG (Specific Gravity) up to 2.0. This completion fluid has very low corrosivity (Corrosivity), which is stable at very high temperatures and high pressures.

scholarly journals ARTIFICIAL NEURAL NETWORK APPLIED IN FORECASTING THE COMPOSITION OF MUNICIPAL SOLID WASTE IN IASI, ROMANIA

2021 ◽  
Vol 29 (3) ◽  
pp. 368-380
Author(s):  
Cristina Ghinea ◽  
Petronela Cozma ◽  
Maria Gavrilescu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Ann Model

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

DRYING KINETICS AND MOISTURE DIFFUSIVITY OF FOUR VARIETIES OF BAMBARA BEANS

2021 ◽  
Vol 6 (1) ◽  
pp. 30-33
Author(s):  
E.O. Awotona ◽  
A.O. Alade ◽  
S.A. Adebanjo ◽  
O. Duduyemi ◽  
T.J. Afolabi
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Moisture Diffusivity ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Fit Statistics ◽  
Drying Behavior ◽  
Maximum Coefficient

Drying of bambara beans was studied at 40oC at every 30 minutes in a Laboratory oven. Effective moisture diffusivity ranges between 5.886 x 10-10 m2/s – 4.354 x 10-10 m2/s respectively. The statistical criteria used in evaluation of the model were maximum coefficient of determination R2 and minimum root mean square error [RMSE]. Determination for goodness of fit statistics for drying of the beans was carried out. Midilli model was used to predict the drying curve. The Midili model was found to produce accurate predictions for all the four varieties of bambara beans and the model was shown to be an excellent model for predicting drying behavior of TVSU-47 and the R2 value was 0.9971 and the value of root mean square error was 0.0149 respectively.

scholarly journals An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

2015 ◽  
Vol 27 (3) ◽  
pp. 217-225 ◽  
Author(s):  
Muhammed Yasin Çodur ◽  
Ahmet Tortum
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Traffic Accidents ◽  
Model Parameters ◽  
Mean Square ◽  
Ann Model ◽  
Accident Prediction ◽  
Marquardt Algorithm ◽  
Artificial Neural

This study presents an accident prediction model of Erzurum’s Highways in Turkey using artificial neural network (ANN) approaches. There are many ANN models for predicting the number of accidents on highways that were developed using 8 years with 7,780 complete accident reports of historical data (2005-2012). The best ANN model was chosen for this task and the model parameters included years, highway sections, section length (km), annual average daily traffic (AADT), the degree of horizontal curvature, the degree of vertical curvature, traffic accidents with heavy vehicles (percentage), and traffic accidents that occurred in summer (percentage). In the ANN model development, the sigmoid activation function was employed with Levenberg-Marquardt algorithm. The performance of the developed ANN model was evaluated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination (R2). The model results indicate that the degree of vertical curvature is the most important parameter that affects the number of accidents on highways.

scholarly journals Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

2020 ◽  
Vol 13 (02) ◽  
pp. 2050009
Author(s):  
Amorndej Puttipipatkajorn ◽  
Amornrit Puttipipatkajorn
Keyword(s):  
Moisture Content ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Least Square ◽  
Mean Square ◽  
Ann Model ◽  
Rubber Sheet ◽  
Calibration Models

Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries. The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside, but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading. Herein, we developed a rapid, robust and nondestructive near-infrared spectroscopy (NIRS)-based method for moisture content determination in rubber sheets. A set of 300 rubber sheets were divided into a calibration (200 samples) and prediction groups (100 samples). The calibration set was used to develop NIRS calibration equation using different calibration models, Partial Least Square Regression (PLSR), Least Square Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Among the models investigated, the ANN model with the first derivative of spectral preprocessing presented the best prediction with a coefficient of determination ([Formula: see text] of 0.993, root mean square error of calibration (RMSEC) of 0.126% and root mean square error of prediction (RMSEP) of 0.179%. The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.

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