Prediction of Rate of Penetration for wells at Nam Con Son basin using Artificial Neural Networks models

The rate of penetration (ROP) is an important parameter that affects the success of a drilling operation. In this paper, the research approach is based on different artificial neural network (ANN) models to predict ROP for oil and gas wells in Nam Con Son basin. The first is the process of collecting and evaluating drilling parameters as input data of the model. Next is to find the network model capable of predicting ROP most accurately. After that, the study will evaluate the number of input parameters of the network model. The ROP prediction results obtained from different ANN models are also compared with traditional models such as the Bingham model, Bourgoyne & Young model. These results have shown the competitiveness of the ANN model and its high applicability to actual drilling operations.

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Use of Artificial Neural Networks in predicting rate of penetration and optimization weight on bit for several wells in Nam Rong - Doi Moi field, Vietnam

Journal of Mining and Earth Sciences ◽

10.46326/jmes.2021.62(3a).05 ◽

2021 ◽

Vol 62 (3a) ◽

pp. 37-47

Author(s):

Hung Tien Nguyen ◽

Duong Hong Vu ◽

Vinh The Nguyen ◽

Doan Thi Tram ◽

Pham Van Trung ◽

...

Keyword(s):

Research Area ◽

High Accuracy ◽

Ann Model ◽

Rate Of Penetration ◽

Doi Moi ◽

Drilling Parameters ◽

Weight On Bit ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

Obtaining the maximum Rate of Penetration (ROP) by optimization of drilling parameters is the aim of every drilling engineer. This helps to save time, reduces cost and minimizes drilling problems. Since ROP depends on a lot of parameters, it is very difficult to predict it correctly. Therefore, it is necessary and important to investigate a solution for predicting ROP with high accuracy in order to determine the suitable drilling parameters. In this study, a new approach using Artificial Neural Network (ANN) has been proposed to predict ROP from real - time drilling data of several wells in Nam Rong - Doi Moi field with more than 900 datasets included important parameters such as weight on bit (WOB), weight of mud (MW), rotary speed (RPM), stand pipe pressure (SPP), flow rate (FR), torque (TQ). In the process of training the network, algorithms and the number of neurons in the hidden layer were varied to find the optimal model. The ANN model shows high accuracy when comparing to actual ROP, therefore it can be recommended as an effective and suitable method to predict ROP of other wells in research area. Besides, base on the proposed ANN model, authors carried out experiments and determine the optimal weight on bit value for the drilling interval from 1800 to 2300 m of wells in in Nam Rong Doi Moi field.

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Modeling of hourly river water temperatures using artificial neural networks

Water Quality Research Journal ◽

10.2166/wqrjc.2014.007 ◽

2014 ◽

Vol 49 (2) ◽

pp. 144-162 ◽

Cited By ~ 1

Author(s):

Cindie Hebert ◽

Daniel Caissie ◽

Mysore G. Satish ◽

Nassir El-Jabi

Keyword(s):

Water Temperature ◽

Coefficient Of Determination ◽

Ann Model ◽

Air Temperatures ◽

Level Data ◽

Water Level Data ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

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Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique

Sustainability ◽

10.3390/su12041376 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1376 ◽

Cited By ~ 4

Author(s):

Ahmad Al-AbdulJabbar ◽

Salaheldin Elkatatny ◽

Ahmed Abdulhamid Mahmoud ◽

Tamer Moussa ◽

Dhafer Al-Shehri ◽

...

Keyword(s):

Empirical Correlation ◽

The Self ◽

Carbonate Reservoirs ◽

Ann Model ◽

Horizontal Drilling ◽

Rate Of Penetration ◽

Drilling Parameters ◽

Unseen Data ◽

Artificial Neural ◽

Self Adaptive

Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collected from Well-A, where it predicted ROP with AAPE and R values of 5.80% and 0.951, respectively. The developed correlation was then validated using unseen data collected from Well-B, where it predicted ROP with an AAPE of 5.29% and a high R of 0.956. The ANN-based correlation outperformed all previous correlations of ROP estimation that were developed based on linear regression, including a recent model developed by Osgouei that predicted the ROP for the validation data with a high AAPE of 14.60% and a low R of 0.629.

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An Artificial Neural Network for Predicting Rate of Penetration in AL- Khasib Formation – Ahdeb Oil Field

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.5.14 ◽

2020 ◽

pp. 1051-1062

Author(s):

Zaher JabbarAttwan AL Zirej ◽

Hassan Abdul Hadi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Oil Field ◽

Coefficient Of Determination ◽

Drilling Mud ◽

Ann Model ◽

Rate Of Penetration ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

The main objective of this study is to develop a rate of penetration (ROP) model for Khasib formation in Ahdab oil field and determine the drilling parameters controlling the prediction of ROP values by using artificial neural network (ANN). An Interactive Petrophysical software was used to convert the raw dataset of transit time (LAS Readings) from parts of meter-to-meter reading with depth. The IBM SPSS statistics software version 22 was used to create an interconnection between the drilling variables and the rate of penetration, detection of outliers of input parameters, and regression modeling. While a JMP Version 11 software from SAS Institute Inc. was used for artificial neural modeling. The proposed artificial neural network method depends on obtaining the input data from drilling mud logging data and wireline logging data. The data then analyzes it to create an interconnection between the drilling variables and the rate of penetration. The proposed ANN model consists of an input layer, hidden layer and outputs layer, while it applies the tangent function (TanH) as a learning and training algorithm in the hidden layer. Finally, the predicted values of ROP are compared with the measured values. The proposed ANN model is more efficient than the multiple regression analysis in predicting ROP. The obtained coefficient of determination (R2) values using the ANN technique are 0.93 and 0.91 for training and validation sets, respectively. This study presents a new model for predicting ROP values in comparison with other conventional drilling measurements.

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Live to learn: learning rules-based artificial neural network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v21.i1.pp558-565 ◽

2021 ◽

Vol 21 (1) ◽

pp. 558

Author(s):

Aseel Shakir I. Hilaiwah ◽

Hanan Abed Alwally Abed Allah ◽

Basim Akhudir Abbas ◽

Tole Sutikno

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ann Model ◽

Output Layer ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer ◽

Intelligent Model ◽

Human Nervous System

<span>An extensive review of the artificial neural network (ANN) is presented in this paper. Previous studies review the artificial neural network (ANN) based on the approaches (algorithms) used or based on the types of the artificial neural network (ANN). The presented paper reviews the ANN based on the goal of the ANN (methods, and layers), which become the main objective of this paper. As a famous artificial intelligent model, ANN mimics the human nervous system in handling the information transmited by different nodes (also known as neurons) in this model. These nodes are stacked in layers and work collectively to bring about solution to complex problems. Numerous structures exist for ANN and each of these structures is designed to addressa a specific task. Basically, the ANN architecture is comprised of 3 different layers wherein the first layer rpresents the input layer that consist of several input nodes that represent the input parameterfor the model. The hidden layer is te second layer and consists of a hidden layer of neurons. The neurons in this layer are directly connected to the neurons in the output layer. The third layer is the output layer which is the models’ response layer. The output layer neurons have the activation functions for the calculation of the ANN final output. The connection between the nodes in the ANN model is mediated by synaptic weights. This paper is a comprehensive study of ANN models and their layers.</span>

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Artificial Neural Network- (ANN-) Based Proxy Model for Fast Performances’ Forecast and Inverse Schedule Design of Steam-Flooding Reservoirs

Mathematical Problems in Engineering ◽

10.1155/2021/5527259 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yuhui Zhou ◽

Yunfeng Xu ◽

Xiang Rao ◽

Yujie Hu ◽

Deng Liu ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Inverse Design ◽

Ann Model ◽

Production Schedule ◽

Steam Flooding ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Forward Looking

Steam flooding is one of the most effective and mature technology in heavy oil development. In this paper, a numerical simulation technology of steam flooding reservoir based on the finite volume method is firstly established. Combined with the phase change of steam phase, the fully implicit solution for steam flooding is carried out by using adaptive-time-step Newton iteration method. The Kriging method is used for stochastically to generate 4250 geological model samples by considering reservoir heterogeneity, and corresponding production schedule parameters are randomly given; then, these reservoir model samples are handled by the numerical simulation technology to obtain corresponding dynamic production data, which constitute the data for artificial neural network (ANN) training. By using the highly nonlinear global effect of artificial neural network and its powerful self-adaptive and self-learning functions, the forward-looking and inverse design ANN models of steam-flooding reservoirs are established, which provides a new method for rapid prediction of steam-flooding production performance and production schedule parameter design. In 4250 samples, the error of the forward-looking model is basically less than 0.1%, and the error of the inverse design model is generally less than 15%. It fully shows that the ANN models developed in this paper can quickly and effectively predict oil production and design production parameters and have an important guiding role in the implementation of the steam flooding technology. Finally, the forward-looking ANN model is applied to efficiently analyze the influencing factors of steam flooding process, and uncertainty analysis of the inverse design ANN model is conducted by Monte Carlo Simulation to illustrate its robustness. Besides, this paper may provide a reference for the application of neural network models to underground oil and gas reservoir, which is a typical invisible black box.

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Monitoring-System Development of Gillnet Using Artificial Neural Network

Volume 6B: Ocean Engineering ◽

10.1115/omae2020-19011 ◽

2020 ◽

Author(s):

Chungkuk Jin ◽

HanSung Kim ◽

JeongYong Park ◽

MooHyun Kim ◽

Kiseon Kim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Big Data ◽

Learning Algorithm ◽

System Development ◽

Ann Model ◽

Ann Models ◽

Artificial Neural ◽

Input Variables ◽

Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

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An Efficient Approach for Real-Time Prediction of Rate of Penetration in Offshore Drilling

Mathematical Problems in Engineering ◽

10.1155/2016/3575380 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 17

Author(s):

Xian Shi ◽

Gang Liu ◽

Xiaoling Gong ◽

Jialin Zhang ◽

Jian Wang ◽

...

Keyword(s):

Oil And Gas ◽

Bohai Bay ◽

Ann Model ◽

Rate Of Penetration ◽

Oil And Gas Exploration ◽

Formation Type ◽

Artificial Neural Network Ann ◽

Learning Machine ◽

Efficient Learning ◽

Rock Mechanical Properties

Predicting the rate of penetration (ROP) is critical for drilling optimization because maximization of ROP can greatly reduce expensive drilling costs. In this work, the typical extreme learning machine (ELM) and an efficient learning model, upper-layer-solution-aware (USA), have been used in ROP prediction. Because formation type, rock mechanical properties, hydraulics, bit type and properties (weight on the bit and rotary speed), and mud properties are the most important parameters that affect ROP, they have been considered to be the input parameters to predict ROP. The prediction model has been constructed using industrial reservoir data sets that are collected from an oil reservoir at the Bohai Bay, China. The prediction accuracy of the model has been evaluated and compared with the commonly used conventional artificial neural network (ANN). The results indicate that ANN, ELM, and USA models are all competent for ROP prediction, while both of the ELM and USA models have the advantage of faster learning speed and better generalization performance. The simulation results have shown a promising prospect for ELM and USA in the field of ROP prediction in new oil and gas exploration in general, as they outperform the ANN model. Meanwhile, this work provides drilling engineers with more choices for ROP prediction according to their computation and accuracy demand.

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Applying artificial neural networks for modelling ship speed and fuel consumption

Neural Computing and Applications ◽

10.1007/s00521-020-05111-2 ◽

2020 ◽

Vol 32 (23) ◽

pp. 17379-17395 ◽

Cited By ~ 1

Author(s):

Wieslaw Tarelko ◽

Krzysztof Rudzki

Keyword(s):

Decision Support ◽

Decision Support System ◽

Fuel Consumption ◽

Support System ◽

Ann Model ◽

Data Set ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Ship Speed

AbstractThis paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making decisions regarding setting the proper commanded outputs to is extraordinarily difficult. To support such decisions, we have developed a decision support system. Its main elements are the ANN models enabling ship fuel consumption and speed prediction. To collect data needed for building ANN models, sea trials were conducted. In this paper, the decision support system concept, input and variables of the ship driveline system models, and data acquisition methods are presented. Based on them, we developed appropriate ANN models. Subsequently, we performed a quality assessment of the collected data set, data normalization and division of the data set, selection of an ANN model architecture and assessment of their quality.

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Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Energies ◽

10.3390/en11092216 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2216 ◽

Cited By ~ 4

Author(s):

Ravi Kishore ◽

Roop Mahajan ◽

Shashank Priya

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Electrical Energy ◽

Optimization Techniques ◽

Operating Conditions ◽

Ann Model ◽

Predicting Performance ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

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