First Use of Machine Learning for Penetration Rate Optimisation on Elgin Franklin

2021 ◽  
Author(s):  
Daniel O'Leary ◽  
Deirdree Polak ◽  
Roman Popat ◽  
Oliver Eatough ◽  
Tom Brian
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Guideline Implementation ◽  
Integrated Approach ◽  
Change Scenario ◽  
Directional Drilling ◽  
Digital Revolution ◽  
Drilling Performance ◽  
Scenario Analyses ◽  
Weight On Bit

Abstract Optimising the Rate of Penetration (ROP) on Development wells contributes heavily to delivery of projects ahead of schedule and has long been a goal for drilling engineers. Selecting the best parameters to achieve this has often proved difficult due to the extensive quantities of data concerning formation types, bottom-hole assembly (BHA) design and bit specifications. Legacy drilling data can also be vast and not well characterised, making it very difficult to robustly analyse manually. Additionally, multiple stakeholders can each have their own hypotheses on how to improve drilling performance, including bit vendors, directional drilling companies, drilling engineers and offshore supervisors, creating further confusion in this field. Together with its team of data scientists, TotalEnergies E&P UK (TEPUK) has utilised machine learning to analyse field and equipment data and produce guidelines for optimised drilling rate. The machine learning algorithm identifies parameters which have a statistical likelihood of improving ROP performance whilst drilling. The model was developed using offset well data from TotalEnergies' Realtime Support Centre (RTSC) and bit design information. This represented the first use of Machine Learning in the 20+ years of drilling on Elgin Franklin. Adapting to this new data-based method forms part of a wider digital revolution within TEPUK and the Offshore Drilling Industry. In this case, an integrated approach from the data scientists, drilling engineers and supervisors was required to transition to a new way of working. The first trial of using optimised parameters was on a recent Franklin well (F13) in the Cretaceous Chalk formations. The model generated statistically optimised parameter sheets which were strictly executed on site. Within the guideline sheets were suggested ranges of Revolutions per Minute (RPM), Flowrate, Weight on Bit (WOB) and Torque, as well as recommendations for bit blades and cutters. Heatmaps were generated to show what combination of WOB and RPM would likely achieve best ROP in each sub formation. The parameter range defined was specifically narrow to reduce any time spent varying parameters. In practice the new digital approach was successfully adopted offshore and contributed to the delivery of the 12 ½" and 8 ½" sections in record time for the field, resulting in significant savings versus AFE. Following the success of the guideline implementation, steps have been taken to integrate the machine learning model with live incoming data on TotalEnergies' digital drilling online platform. Since the initial trial on Franklin, online ROP optimisation features have been deployed on the Elgin field and currently provide live parameter guidance, a forecast to section TD and data driven bit change scenario analyses whist drilling.

scholarly journals An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 763
Author(s):  
Ran Yang ◽  
Zhenbo Wang ◽  
Jiajia Chen
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Food Product ◽  
Integrated Approach ◽  
Mechanistic Modeling ◽  
Bayesian Optimization ◽  
Initial Training ◽  
Thickness Optimization ◽  
Heating Uniformity ◽  
Food Design

Mechanistic-modeling has been a useful tool to help food scientists in understanding complicated microwave-food interactions, but it cannot be directly used by the food developers for food design due to its resource-intensive characteristic. This study developed and validated an integrated approach that coupled mechanistic-modeling and machine-learning to achieve efficient food product design (thickness optimization) with better heating uniformity. The mechanistic-modeling that incorporated electromagnetics and heat transfer was previously developed and validated extensively and was used directly in this study. A Bayesian optimization machine-learning algorithm was developed and integrated with the mechanistic-modeling. The integrated approach was validated by comparing the optimization performance with a parametric sweep approach, which is solely based on mechanistic-modeling. The results showed that the integrated approach had the capability and robustness to optimize the thickness of different-shape products using different initial training datasets with higher efficiency (45.9% to 62.1% improvement) than the parametric sweep approach. Three rectangular-shape trays with one optimized thickness (1.56 cm) and two non-optimized thicknesses (1.20 and 2.00 cm) were 3-D printed and used in microwave heating experiments, which confirmed the feasibility of the integrated approach in thickness optimization. The integrated approach can be further developed and extended as a platform to efficiently design complicated microwavable foods with multiple-parameter optimization.

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.

Study of the Influence of Shale Anisotropy Orientation on Directional Drilling Performance in Shale

2017 ◽  
Author(s):  
Abdelsalam N. Abugharara ◽  
Charles A. Hurich ◽  
John Molgaard ◽  
Stephen D. Butt
Keyword(s):  
Depth Of Cut ◽  
Directional Drilling ◽  
Laboratory Procedure ◽  
Drilling Rig ◽  
Drilling Performance ◽  
Mechanical Measurements ◽  
Different Types ◽  
Pdc Bit ◽  
Weight On Bit ◽  
Shale Anisotropy

The influence of shale anisotropy orientation on shale drilling performance has been studied using a new laboratory procedure. This procedure includes drilling and testing three sets of shale samples in different orientations from a single rock sample. Shale samples of different types were collected from outcrops located at Conception Bay South (CBS) in Newfoundland, Canada. For predrilling tests, oriented physical and mechanical measurements on each type of shale were conducted on the same rocks that will be drilled later. For drilling tests, three sets of tests were conducted. Each set was in a different orientation, corresponding to those in the physical and mechanical measurements. Each set was conducted under the same drilling parameters of pressure, flow rate (FR), and weight on bit (WOB) using a fully instrumented laboratory scale drilling rig. Two different types of drill bits were used, including a 35 mm dual cutter PDC bit and a 25.4 mm diamond coring bit. The drilling data was analyzed by constructing relationships between drilling rate of penetration (ROP) versus orientation (i.e. 0°, 45°, or 90°). The analysis also included relationships between WOB and bit cutter Depth of Cut (DOC), Revolution Per Minute (RPM), and Torque (TRQ). All the above relations were evaluated as a function of shale bedding orientation. This evaluation can assist in understanding the influence of shale anisotropy on oriented drilling. Details of the conducted tests and results are reported.

scholarly journals Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

2015 ◽  
Vol 112 (38) ◽  
pp. E5351-E5360 ◽  
Author(s):  
Weizhe Hong ◽  
Ann Kennedy ◽  
Xavier P. Burgos-Artizzu ◽  
Moriel Zelikowsky ◽  
Santiago G. Navonne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Social Behaviors ◽  
Integrated Approach ◽  
Video Tracking ◽  
The Body ◽  
Automated Measurement ◽  
Depth Sensing ◽  
Dynamic Interactions ◽  
Video Cameras

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

scholarly journals An Integrated Approach for Drilling Optimization Using Advanced Drilling Optimizer

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
David Hankins ◽  
Saeed Salehi ◽  
Fatemeh Karbalaei Saleh
Keyword(s):  
Integrated Approach ◽  
Economic Benefits ◽  
Operational Parameters ◽  
Drilling Performance ◽  
Drilling Parameters ◽  
Weight On Bit ◽  
Drilling Operations ◽  
Rotary Speed ◽  
Well Data ◽  
Advanced Drilling

The ability to optimize drilling procedures and economics involves simulation to understand the effects operational parameters and equipment design have on the ROP. An analysis applying drilling performance modeling to optimize drilling operations has been conducted to address this issue. This study shows how optimum operational parameters and equipment can be predicted by simulating drilling operations of preexisting wells in a Northwest Louisiana field. Reference well data was gathered and processed to predict the “drillability” of the formations encountered by inverting bit specific ROP models to solve for rock strength. The output data generated for the reference well was formatted to simulate upcoming wells. A comparative analysis was conducted between the predicted results and the actual results to show the accuracy of the simulation. A significant higher accuracy is shown between the simulated and actual drilling results. Once simulations were validated, optimum drilling parameters and equipment specifications were found by varying different combinations of weight on bit (WOB), rotary speed (RPM), hydraulics, and bit specifications until the highest drilling rate is achieved for each well. A qualitative and quantitative analysis of the optimized results was conducted to assess the potential operational and economic benefits on drilling operations.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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