scholarly journals Conception of a Web Operation System for Processing Petroleum Related Drilling Data: A Focus on Pre-Salt Real-Time Automation and Optimization

2019 ◽  
Vol 12 (04) ◽  
pp. 61-71
Author(s):  
Yuri Soares Pinheiro ◽  
Lucas Campos Vieira ◽  
Andreas Nascimento ◽  
Francisco de Assis Souza dos Santos ◽  
Mauro Hugo Mathias ◽  
...  
Keyword(s):  
Real Time ◽  
Operation System ◽  
Drilling Data ◽  
Processing Petroleum

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.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

Rock Strength Prediction in Real-Time while Drilling Employing Random Forest and Functional Network Techniques

2021 ◽  
pp. 1-21
Author(s):  
Hany Gamal ◽  
Ahmed Alsaihati ◽  
Salaheldin Elkatatny ◽  
Saleh Haidary ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Rock Strength ◽  
Prediction Models ◽  
Functional Network ◽  
Percentage Error ◽  
Data Set ◽  
Unseen Data ◽  
Drilling Data ◽  
Data Points

Abstract The rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by conventional methods such as experimental work or empirical correlation from logging data are time consuming and highly cost. To overcome these drawbacks, this paper utilized the help of artificial intelligence (AI) to predict (in a real-time) the rock strength from the drilling parameters using two AI tools. Random forest (RF) based on principal component analysis (PCA), and functional network (FN) techniques were employed to build two UCS prediction models based on the drilling data such as weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q), and the rate of penetration (ROP). The models were built using 2,333 data points from well (A) with 70:30 training to testing ratio. The models were validated using unseen data set (1,300 data points) of Well (B) which is located in the same field and drilled across the same complex lithology. The results of the PCA-based RF model outperformed the FN in terms of correlation coefficient (R) and average absolute percentage error (AAPE). The overall accuracy for PCA-based RF was R of 0.99 and AAPE of 4.3 %, and for FN yielded R of 0.97 and AAPE of 8.5%. The validation results showed that R was 0.99 for RF and 0.96 for FN, while the AAPE was 4 and 7.9 % for RF and FN models, respectively. The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost and enhancing the well stability by generating UCS log from the rig drilling data.

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.

Sign in / Sign up

Export Citation Format

Share Document