Least squares modeling of the depth of cut of reservoir rock from real-time drilling parameters

2021 ◽  
pp. 1-18
Author(s):  
Wilson Ekpotu ◽  
Joseph Akintola ◽  
Martins Obialor ◽  
Ayodeji Ayoola ◽  
Michael Asama ◽  
...  
Keyword(s):  
Least Squares ◽  
Real Time ◽  
Reservoir Rock ◽  
Depth Of Cut ◽  
Drilling Parameters

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

2021 ◽  
Author(s):  
Ahmed Al-Sabaa ◽  
Hany Gamal ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Real Time ◽  
Storage Capacity ◽  
Data Set ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Rock Porosity ◽  
Data Points ◽  
Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

Application of Machine Learning for Lithology-on-Bit Prediction using Drilling Data in Real-Time

2021 ◽  
Author(s):  
Temirlan Zhekenov ◽  
Artem Nechaev ◽  
Kamilla Chettykbayeva ◽  
Alexey Zinovyev ◽  
German Sardarov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Hybrid Modeling ◽  
Data Set ◽  
Drilling Parameters ◽  
Drilling Data ◽  
Stable Solutions ◽  
Mud Logging

SUMMARY Researchers base their analysis on basic drilling parameters obtained during mud logging and demonstrate impressive results. However, due to limitations imposed by data quality often present during drilling, those solutions often tend to lose their stability and high levels of predictivity. In this work, the concept of hybrid modeling was introduced which allows to integrate the analytical correlations with algorithms of machine learning for obtaining stable solutions consistent from one data set to another.

The Use of a Sensor Modules System for Measuring Drilling Parameters in a Bit, Significantly Reduces the Construction Time of Wells in Eastern Siberia

2021 ◽  
Author(s):  
Andrey Alexandrovich Rebrikov ◽  
Anton Anatolyevich Koschenkov ◽  
Anastasiya Gennadievna Rakina ◽  
Igor Dmitrievich Kortunov ◽  
Nikita Vladimirovich Koshelev ◽  
...  
Keyword(s):  
Oil And Gas ◽  
New Technologies ◽  
Field Tests ◽  
Depth Of Cut ◽  
Drilling Process ◽  
Eastern Siberia ◽  
Construction Time ◽  
Stage Of Development ◽  
Drilling Parameters ◽  
Pdc Bit

Abstract Currently, production and exploration drilling has entered a stage of development where one of the highest priority goals is to reduce the time for well construction with new technologies and innovations. One of the key components in this aspect is the utilizing of the latest achievements in the design and manufacture of rock cutting tools – drill bits. This article presents some new ideas on methods for identifying different types of vibrations when drilling with PDC bits using a system of sensors installed directly into the bit itself. In the oil and gas fields of Eastern Siberia, one of the main reasons for ineffective drilling with PDC bits are vibrations, which lead to premature wear of the cutting structure of the bit and the achievement of low ROPs in the dolomite and dolerite intervals. For efficient drilling of wells of various trajectories with a bottom hole assembly (BHA), including a downhole motor (PDM) and a PDC bit, special attention is paid to control of the bit by limiting the depth of cut, as well as the level of vibrations that occur during drilling process. Often, the existing complex of surface and BHA equipment fails to identify vibrations that occur directly on the bit, as well as to establish the true cause of their occurrence. Therefore, as an innovative solution to this problem, a system of sensors installed directly into the bit itself is proposed. The use of such a system makes it possible to determine the drilling parameters, differentiated depending on the lithological properties of rocks, leading to an increase in vibration impact. Together with the Operators, tests have been successfully carried out, which have proven the effectiveness of the application of this technology. The data obtained during the field tests made it possible to determine the type and source of vibration very accurately during drilling. In turn, this made it possible to precisely adjust the drilling parameters according to the drilled rocks, to draw up a detailed road map of effective drilling in a specific interval. Correction of drilling parameters based on the analysis of data obtained from sensors installed in the bit made it possible to reduce the resulting wear of the PDC bit cutting structure and, if necessary, make changes to the bit design to improve the technical and economic indicators. Thus, the use of a system of sensors for measuring the drilling parameters in a bit ensured the dynamic stability of the entire BHA at the bottomhole when drilling in rocks of different hardness, significantly reduced the wear of the drilling tools and qualitatively improved the drilling performance.

scholarly journals Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Abdulmalek Ahmed ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali ◽  
Mahmoud Abughaban ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Drilling Fluid ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Functional Networks ◽  
Lost Circulation ◽  
Drilling Parameters ◽  
High Pressure High Temperature ◽  
Drilling Cost

Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (RMSE), confusion matrix, and correlation coefficient (R). All the AI models identified the lost circulation zones in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09. ANN is the most accurate model with R=0.99 and RMSE=0.05. An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with R=0.946 and RMSE=0.165 and R=0.952 and RMSE=0.155, respectively.

Real-Time Diagnosis of the Exhaust Recirculation in Diesel Engines Using Least-Squares Parameter Estimation

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
Javad Mohammadpour ◽  
Karolos Grigoriadis ◽  
Matthew Franchek ◽  
Benjamin J. Zwissler
Keyword(s):  
Diesel Engine ◽  
Least Squares ◽  
Real Time ◽  
Diesel Engines ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Low Flow ◽  
Robust Version ◽  
Orifice Flow ◽  
Gas Recirculation

In this paper, we present a real-time parameter identification approach for diagnosing faults in the exhaust gas recirculation (EGR) system of Diesel engines. The proposed diagnostics method has the ability to detect and estimate the magnitude of a leak or a restriction in the EGR valve, which are common faults in the air handling system of a Diesel engine. Real-time diagnostics is achieved using a recursive-least-squares (RLS) method, as well as, a recursive formulation of a more robust version of the RLS method referred to as recursive total-least-squares method. The method is used to identify the coefficients in a static orifice flow model of the EGR valve. The proposed approach of fault detection is successfully applied to diagnose low-flow or high-flow faults in an engine and is validated using experimental data obtained from a Diesel engine test cell and a truck.

Statistical Analysis of Process Parameters in Drilling of Al/Al2O3p Metal Matrix Composites

2014 ◽  
Vol 14 (3) ◽  
pp. 171-175 ◽  
Author(s):  
Yashvir Singh ◽  
Amneesh Singla ◽  
Ajay Kumar
Keyword(s):  
Surface Roughness ◽  
Statistical Analysis ◽  
Process Parameters ◽  
Metal Matrix ◽  
Design Method ◽  
Experimental Studies ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Signal To Noise ◽  
Drilling Parameters

AbstractThis paper presents a statistical analysis of process parameters for surface roughness in drilling of Al/Al2O3p metal matrix composite. The experimental studies were conducted under varying spindle speed, feed rate, point angle of drill. The settings of drilling parameters were determined by using Taguchi experimental design method. The level of importance of the drilling parameters is determined by using analysis of variance. The optimum drilling parameter combination was obtained by using the analysis of signal-to-noise ratio. Through statistical analysis of response variables and signal-to-noise ratios, the determined significant factors are depth of cut and drill point angle with the contributions of 87% and 12% respectively, whereas the cutting speed is insignificant contributing by 1% only. Confirmation tests verified that the selected optimal combination of process parameter through Taguchi design was able to achieve desired surface roughness.

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