Coal identification using neural networks with real-time coalbed methane drilling data

2019 ◽  
Vol 59 (1) ◽  
pp. 319 ◽  
Author(s):  
Ruizhi Zhong ◽  
Raymond Johnson Jr ◽  
Zhongwei Chen ◽  
Nathaniel Chand
Keyword(s):  
Real Time ◽  
Coalbed Methane ◽  
Gamma Ray ◽  
Ann Model ◽  
Drilling Data ◽  
Weight On Bit ◽  
Net To Gross ◽  
Rotary Speed ◽  
Artificial Neural Network Ann ◽  
Log Interpretation

Currently, coal is identified using coring data or log interpretation. Coring is the most dependable methodology, but it is costly and its characterisation is expensive and time consuming. Logging methods are convenient, reliable, and reproducible, but can be subject to statistical and shouldering effects and often have operational difficulties in deviated or horizontal wells. Drilling data, which are routinely available, can potentially be used to identify coal sections in a machine learning environment when conventional wireline logs are not available. To achieve this, a four-layer artificial neural network (ANN) was used to identify coals in a well at Walloon Sub-Group, Surat Basin. The ANN model used drilling data and some logging-while-drilling (LWD) data. The inputs for the lithological model from high-frequency drilling data include weight on bit, rotary speed, torque, and rate of penetration. Inputs from LWD data include gamma ray and hole diameter. The criterion for coal identification is based on bulk density cutoff. The simulation results show that the ANN can deliver an overall accuracy of 96%. Due to the low net-to-gross ratio of coals within the Walloon sequence, a lower but reasonable F1 score of 0.78 is achievable for the coal sections. The proposed model can potentially be implemented in real-time to identify coal intervals without additional logs and aid validation of minimal log data.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Yanfang Wang ◽  
Saeed Salehi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Field Data ◽  
Ann Model ◽  
Time Data ◽  
Neural Network Approach ◽  
Lost Circulation ◽  
Drilling Performance ◽  
Artificial Neural Network Ann ◽  
Pump Pressure

Real-time drilling optimization improves drilling performance by providing early warnings in operation Mud hydraulics is a key aspect of drilling that can be optimized by access to real-time data. Different from the investigated references, reliable prediction of pump pressure provides an early warning of circulation problems, washout, lost circulation, underground blowout, and kicks. This will help the driller to make necessary corrections to mitigate potential problems. In this study, an artificial neural network (ANN) model to predict hydraulics was implemented through the fitting tool of matlab. Following the determination of the optimum model, the sensitivity analysis of input parameters on the created model was investigated by using forward regression method. Next, the remaining data from the selected well samples was applied for simulation to verify the quality of the developed model. The novelty is this paper is validation of computer models with actual field data collected from an operator in LA. The simulation result was promising as compared with collected field data. This model can accurately predict pump pressure versus depth in analogous formations. The result of this work shows the potential of the approach developed in this work based on NN models for predicting real-time drilling hydraulics.

Drilling in the Digital Age: Machine Learning Assisted Bit Selection and Optimization

2021 ◽  
Author(s):  
Peter Batruny ◽  
Hafiz Zubir ◽  
Pete Slagel ◽  
Hanif Yahya ◽  
Zahid Zakaria ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monte Carlo Analysis ◽  
Specific Model ◽  
Planning Decisions ◽  
Drilling Performance ◽  
Weight On Bit ◽  
Data Points ◽  
Ideal Tool ◽  
Rotary Speed ◽  
Artificial Neural Network Ann

Abstract Conventionally, a bit is selected from offset well bit run summaries. This method of selection is not always accurate since each bit is run under different conditions which might not be reflected in an offset study analysis. The large quantities of data generated from real time measurements in offset wells makes machine learning the ideal tool for analysis and comparison. Artificial Neural Network (ANN) is a relatively simple machine learning tool that combines inputs and calculation layers to compute a specified output layer. The ANN is fed over thousands of data points from 17-1/2 in hole sections across multiple wells. A specific model is then trained for every bit with weight on bit (WOB), rotary speed (RPM), bit hydraulics, and lithological properties as inputs and rate of penetration (ROP) as output. The model is finalized when a satisfactory statistical set of KPI's are achieved. Using a combination of Monte-Carlo analysis and sensitivity analysis, different bits are compared by varying parameters for the same bit and varying the bit under the same parameters. A bit and its optimized parameters are proposed, resulting in an average instantaneous ROP improvement of 32%. Performance benchmarked with individual drilling parameters shows improved ROP response to WOB, RPM, and bit hydraulics in the optimized run. This project solidifies machine learning as a powerful tool for bit selection and parameter optimization to improve drilling performance. Machine learning will become a significant part of well planning, design, and operations in the future. This study demonstrates how ANN's can be used to learn from previous operations and influence planning decisions to improve bit performance.

Real-Time Software to Estimate Friction Coefficient and Downhole Weight on Bit During Drilling of Horizontal Wells

2014 ◽  
Author(s):  
Mazeda Tahmeen ◽  
Geir Hareland ◽  
Bernt S. Aadnoy
Keyword(s):  
Friction Coefficient ◽  
Real Time ◽  
Information Transfer ◽  
Horizontal Wells ◽  
Drill String ◽  
Surface Measurement ◽  
Drilling Process ◽  
Drilling Data ◽  
Weight On Bit ◽  
Drag Analysis

The increasing complexity and higher drilling cost of horizontal wells demand extensive research on software development for the analysis of drilling data in real-time. In extended reach drilling, the downhole weight on bit (WOB) differs from the surface seen WOB (obtained from on an off bottom hookload difference reading) due to the friction caused by drill string movement and rotation in the wellbore. The torque and drag analysis module of a user-friendly real-time software, Intelligent Drilling Advisory system (IDAs) can estimate friction coefficient and the effective downhole WOB while drilling. IDAs uses a 3-dimensional wellbore friction model for the analysis. Based on this model the forces applied on a drill string element are buoyed weight, axial tension, friction force and normal force perpendicular to the contact surface of the wellbore. The industry standard protocol, WITSML (Wellsite Information Transfer Standard Markup Language) is used to conduct transfer of drilling data between IDAs and the onsite or remote WITSML drilling data server. IDAs retrieves real-time drilling data such as surface hookload, pump pressure, rotary RPM and surface WOB from the data servers. The survey data measurement for azimuth and inclination versus depth along with the retrieved drilling data, are used to do the analysis in different drilling modes, such as lowering or tripping in and drilling. For extensive analysis the software can investigate the sensitivity of friction coefficient and downhole WOB on user-defined drill string element lengths. The torque and drag analysis module, as well as the real-time software, IDAs has been successfully tested and verified with field data from horizontal wells drilled in Western Canada. In the lowering mode of drilling process, the software estimates the overall friction coefficient when the drill bit is off bottom. The downhole WOB estimated by the software is less than the surface measurement that the drillers used during drilling. The study revealed verification of the software by comparing the estimated downhole WOB with the downhole WOB recorded using a downhole measuring tool.

Looking Ahead of the Bit Using Surface Drilling and Petrophysical Data: Machine-Learning-Based Real-Time Geosteering in Volve Field

SPE Journal ◽  
2020 ◽  
Vol 25 (02) ◽  
pp. 990-1006 ◽  
Author(s):  
Ishank Gupta ◽  
Ngoc Tran ◽  
Deepak Devegowda ◽  
Vikram Jayaram ◽  
Chandra Rai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Gamma Ray ◽  
Cost Effective ◽  
Data Set ◽  
The North ◽  
Public Data ◽  
Weight On Bit ◽  
Logging While Drilling ◽  
Rock Mechanical Properties

Summary Petroleum reservoirs are often associated with multiple target zones or a single zone adjacent to nonproductive intervals. Real-time geosteering therefore becomes important to remain in zone or to dynamically steer toward a target. This requires knowledge of the petrophysical/rock mechanical properties of the rock surrounding the bit. Although logging while drilling can provide this information, a cost-effective and almost-real-time solution is lacking. In general, there is a depth lag, and therefore, a time delay, between what the logging-while-drilling sub relays to the surface and the bit performance. This study focuses on relating drill-bit- and drillstring-performance data in a machine-learning (ML) workflow to predict the lithology at the bit while drilling. The method we are proposing offers several advantages in terms of cost and time savings for real-time geosteering applications, where going out of zone requires costly intervention. In this study, we have used a public data set from Volve Field on the Norwegian continental shelf. Within our proposed workflow, as a first step, logs sensitive to lithology [such as density, gamma ray (GR), and sonic] are grouped into three electrofacies. We also had access to core data, which helped us interpret the electrofacies in terms of mineralogy. The three electrofacies corresponded to quartz-rich (sandstone/siltstone), clay-rich (shale), and carbonate-rich (limestone) lithologies. The next step is to predict the electrofacies using various measurement-while-drilling (MWD) variables, such as rate of penetration (ROP), weight on bit (WOB), and several others that are monitored in real time. Supervised classification algorithms were used to relate real-time surface measurements to lithology. The algorithms were able to predict lithology in test wells with more than 80% accuracy. These results, although encouraging, constitute a small step toward drilling-automation/advisory systems. The development of such systems can prevent costly out-of-zone drilling and minimize rig time and equipment use, thereby potentially reducing capital expenditures. This study was specifically performed in Volve Field in the North Sea using petrophysical and surface drilling data from vertical wells. However, the workflow has a potential to be extended to other formations in other fields in different well types.

scholarly journals A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 250
Author(s):  
Ahmed Abdelmoamen Ahmed ◽  
Gbenga Agunsoye
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Network Traffic ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Ann Model ◽  
K Nearest Neighbors ◽  
Artificial Neural Network Ann ◽  
User Friendly ◽  
Network Administrators

The increasing ubiquity of network traffic and the new online applications’ deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their networks. However, modern network traffic uses dynamic ports and is transported over secure application-layer protocols (e.g., HTTPS, SSL, and SSH). This makes it a challenging task for network administrators to identify online applications using traditional port-based approaches. One way for classifying the modern network traffic is to use machine learning (ML) to distinguish between the different traffic attributes such as packet count and size, packet inter-arrival time, packet send–receive ratio, etc. This paper presents the design and implementation of NetScrapper, a flow-based network traffic classifier for online applications. NetScrapper uses three ML models, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Network (ANN), for classifying the most popular 53 online applications, including Amazon, Youtube, Google, Twitter, and many others. We collected a network traffic dataset containing 3,577,296 packet flows with different 87 features for training, validating, and testing the ML models. A web-based user-friendly interface is developed to enable users to either upload a snapshot of their network traffic to NetScrapper or sniff the network traffic directly from the network interface card in real time. Additionally, we created a middleware pipeline for interfacing the three models with the Flask GUI. Finally, we evaluated NetScrapper using various performance metrics such as classification accuracy and prediction time. Most notably, we found that our ANN model achieves an overall classification accuracy of 99.86% in recognizing the online applications in our dataset.

scholarly journals 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

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.

Machine Learning Application to Estimate Missing Rock Mechanical Logs and Reduce Uncertainty While Drilling

2021 ◽  
Author(s):  
Ardiansyah Negara ◽  
Arturo Magana-Mora ◽  
Khaqan Khan ◽  
Johannes Vossen ◽  
Guodong David Zhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Models ◽  
Gamma Ray ◽  
Well Logging ◽  
Complex Model ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Time Data ◽  
Drilling Data

Abstract This study presents a data-driven approach using machine learning algorithms to provide predicted analogues in the absence of acoustic logs, especially while drilling. Acoustic logs are commonly used to derive rock mechanical properties; however, these data are not always available. Well logging data (wireline/logging while drilling - LWD), such as gamma ray, density, neutron porosity, and resistivity, are used as input parameters to develop the data-driven rock mechanical models. In addition to the logging data, real-time drilling data (i.e., weight-on-bit, rotation speed, torque, rate of penetration, flowrate, and standpipe pressure) are used to derive the model. In the data preprocessing stage, we labeled drilling and well logging data based on formation tops in the drilling plan and performed data cleansing to remove outliers. A set of field data from different wells across the same formation is used to build and train the predictive models. We computed feature importance to rank the data based on the relevance to predict acoustic logs and applied feature selection techniques to remove redundant features that may unnecessarily require a more complex model. An additional feature, mechanical specific energy, is also generated from drilling real-time data to improve the prediction accuracy. A number of scenarios showing a comparison of different predictive models were studied, and the results demonstrated that adding drilling data and/or feature engineering into the model could improve the accuracy of the models.

Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data-Quality

2022 ◽  
pp. 1-14
Author(s):  
Salem Al-Gharbi ◽  
Abdulaziz Al-Majed ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Oil And Gas ◽  
Expert Knowledge ◽  
Cost Effective ◽  
Learning Technologies ◽  
Support Vector ◽  
Drilling Data ◽  
Artificial Neural Network Ann

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote environments conducting unconventional operations. In order to maintain safe, fast and more cost-effective operations, utilizing machine learning (ML) technologies has become a must. The harsh environments of drilling sites and the transmission setups, are negatively affecting the drilling data, leading to less than acceptable ML results. For that reason, big portion of ML development projects were actually spent on improving the data by data-quality experts. The objective of this paper is to evaluate the effectiveness of ML on improving the real-time drilling-data-quality and compare it to a human expert knowledge. To achieve that, two large real-time drilling datasets were used; one dataset was used to train three different ML techniques: artificial neural network (ANN), support vector machine (SVM) and decision tree (DT), the second dataset was used to evaluate it. The ML results were compared with the results of a real- time drilling data quality expert. Despite the complexity of ANN and good results in general, it achieved a relative root mean square error (RRMSE) of 2.83%, which was lower than DT and SVM technologies that achieved RRMSE of 0.35% and 0.48% respectively. The uniqueness of this work is in developing ML that simulates the improvement of drilling-data- quality by an expert. This research provides a guide for improving the quality of real-time drilling data.

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