scholarly journals Implementation of artificial intelligence and support vector machine learning to estimate the drilling fluid density in high-pressure high-temperature wells

2021 ◽  
Vol 7 ◽  
pp. 4106-4113
Author(s):  
Rahmad Syah ◽  
Naeim Ahmadian ◽  
Marischa Elveny ◽  
S.M. Alizadeh ◽  
Meysam Hosseini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
High Pressure ◽  
High Temperature ◽  
Drilling Fluid ◽  
Support Vector ◽  
Fluid Density ◽  
High Pressure High Temperature

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

Bloodless Technique to Detect Diabetes using Soft Computational Tool

2015 ◽  
pp. 139-158
Author(s):  
Puspalata Sah ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Cotton Wool ◽  
Research Issue ◽  
Important Research ◽  
Cotton Wool Spots ◽  
Essential Components ◽  
Soft Computing Techniques

Detection of diabetes using bloodless technique is an important research issue in the area of machine learning and artificial intelligence (AI). Here we present the working of a system designed to detect the abnormality of the eye with pain and blood free method. The typical features for diabetic retinopathy (DR) are used along with certain soft computing techniques to design such a system. The essential components of DR are blood vessels, red lesions visible as microaneurysms, hemorrhages and whitish lesions i.e., lipid exudates and cotton wool spots. The chapter reports the use of a unique feature set derived from the retinal image of the eye. The feature set is applied to a Support Vector Machine (SVM) which provides the decision regarding the state of infection of the eye. The classification ability of the proposed system for blood vessel and exudate is 91.67% and for optic disc and microaneurysm is 83.33%.

scholarly journals Enhancing the Stability of Invert Emulsion Drilling Fluid for Drilling in High-Pressure High-Temperature Conditions

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2393 ◽  
Author(s):  
Salaheldin Elkatatny
Keyword(s):  
High Pressure ◽  
High Temperature ◽  
Rheological Properties ◽  
Drilling Fluid ◽  
Electrical Stability ◽  
Dynamic Conditions ◽  
High Pressure High Temperature ◽  
Invert Emulsion ◽  
The Stability ◽  
Filtration Properties

Drilling in high-pressure high-temperature (HPHT) conditions is a challenging task. The drilling fluid should be designed to provide high density and stable rheological properties. Barite is the most common weighting material used to adjust the required fluid density. Barite settling, or sag, is a common issue in drilling HPHT wells. Barite sagging may cause many problems such as density variations, well-control problems, stuck pipe, downhole drilling fluid losses, or induced wellbore instability. This study assesses the effect of using a new copolymer (based on styrene and acrylic monomers) on the rheological properties and the stability of an invert emulsion drilling fluid, which can be used to drill HPHT wells. The main goal is to prevent the barite sagging issue, which is common in drilling HPHT wells. A sag test was performed under static (vertical and 45° incline) and dynamic conditions in order to evaluate the copolymer’s ability to enhance the suspension properties of the drilling fluid. In addition, the effect of this copolymer on the filtration properties was performed. The obtained results showed that adding the new copolymer with 1 lb/bbl concentration has no effect on the density and electrical stability. The sag issue was eliminated by adding 1 lb/bbl of the copolymer to the invert emulsion drilling fluid at a temperature >300 °F under static and dynamic conditions. Adding the copolymer enhanced the storage modulus by 290% and the gel strength by 50%, which demonstrated the power of the new copolymer to prevent the settling of the barite particles at a higher temperature. The 1 lb/bbl copolymer’s concentration reduced the filter cake thickness by 40% at 400 °F, which indicates the prevention of barite settling at high temperature.

Prediction of High-Pressure/High-Temperature Rheological Properties of Drilling Fluids from the Viscosity Data Measured on a Coaxial Cylinder Viscometer

SPE Journal ◽  
2021 ◽  
pp. 1-22
Author(s):  
Sidharth Gautam ◽  
Chandan Guria ◽  
Laldeep Gope
Keyword(s):  
High Pressure ◽  
High Temperature ◽  
Shear Viscosity ◽  
Drilling Fluid ◽  
Viscosity Model ◽  
Drilling Fluids ◽  
Viscosity Data ◽  
Monitoring And Control ◽  
High Pressure High Temperature ◽  
Proposed Model

Summary Determining the rheology of drilling fluid under subsurface conditions—that is, pressure > 103.4 MPa (15,000 psi) and temperature > 450 K (350°F)—is very important for safe and trouble-free drilling operations of high-pressure/high-temperature (HP/HT) wells. As the severity of HP/HT wells increases, it is challenging to measure downhole rheology accurately. In the absence of rheology measurement tools under HP/HT conditions, it is essential to develop an accurate rheological model under extreme conditions. In this study, temperature- and pressure-dependence rheology of drilling fluids [i.e., shear viscosity, apparent viscosity (AV), and plastic viscosity (PV)] are predicted at HP/HT conditions using the fundamental momentum transport mechanism (i.e., kinetic theory) of liquids. Drilling fluid properties (e.g., density, thermal decomposition temperature, and isothermal compressibility), and Fann® 35 Viscometer (Fann Instrument Corporation, Houston, USA) readings at surface conditions, are the only input parameters for the proposed HP/HT shear viscosity model. The proposed model has been tested using 26 different types of HP/HT drilling fluids, including water, formate, oil, and synthetic oil as base fluids. The detailed error and the sensitivity analysis have been performed to demonstrate the accuracy of the proposed model and yield comparative results. The proposed model is quite simple and may be applied to accurately predict the rheology of numerous drilling fluids. In the absence of subsurface rheology under HP/HT conditions, the proposed viscosity model may be used as a reliable soft-sensor tool for the online monitoring and control of rheology under downhole conditions while drilling HP/HT wells.

Research on Calculation Method of Density and Rheology of High-Temperature High-Pressure Drilling Fluid

2021 ◽  
Author(s):  
Lei Wang ◽  
Jin Yang ◽  
Zhengkang Li ◽  
Xinyue He ◽  
Lei Li ◽  
...  
Keyword(s):  
High Pressure ◽  
High Temperature ◽  
Oil And Gas ◽  
Drilling Fluid ◽  
Absolute Error ◽  
Rheological Parameters ◽  
Fluid Density ◽  
Maximum Absolute Error ◽  
Deep Well ◽  
High Temperature High Pressure

Abstract With the strengthening of exploration and development of deep strata and offshore oil and gas resources, more and more deep wells and deep-water wells have put forward higher requirements for drilling fluid performance. The high-temperature high-pressure of deep well and the low temperature environment of deep well have important influence on the rheology and density of drilling fluid. A new method for calculating the rheology and density of high -temperature high-pressure (HTHP) drilling fluid is proposed and studied in this paper. In this paper, the HTHP rheological data are used to predict the shear stress under different shear rates, and then the wellbore rheological parameters are predicted and analyzed. For the calculation of drilling fluid density, the classical component method static density calculation model established by Hoberock model based on drilling fluid components is analyzed and improved in this paper. The obtained model predicts that the maximum absolute error of drilling fluid density under different temperature and pressure is 0.02 g/cm3, and the absolute error is controlled within 2 %.

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