TY - THES

T1 - Conceptual Design of Drilling Cuttings Analysis System Based on Machine Learning Techniques

AU - Iastrebov, Pavel

N1 - embargoed until null

PY - 2020

Y1 - 2020

N2 - Analyzing return cuttings during drilling is one of the opportunities, besides core analysis, to observe and characterize the drilled rock. It gives real time information needed for bit depth correction and lithology correlation, such as rock type, color, texture (grain size, shape and sorting), cement amount, fossils presence, porosity and permeability. Correct measurements of those parameters (shape and size distribution in particular) improves the drilling performance and anticipates possible problems and complications. Cuttings and cavings presence in annular space increase the Equivalent Circulating Density (ECD), which leads to higher pressure losses; they are also one of the causes of Rate of Penetration (ROP) reduction because of chip hold down effect. Their shape is the inference for probable causes of borehole instability and quality of the mud cake. Several techniques have been used in last decades for obtaining the return cuttings parameters, such as their relative amount, particle size distribution (PSD), size and shape. They comprise state of the art technology based on computer vision techniques with machine learning algorithms as a software. A number of such techniques is already available on the market, and have their limitations and advantages. Basing on this principle, OMV is planning to build in house intelligent and cost-effective system which is capable of determining the cuttings parameters in real time. The built system should be feasible from the point of proactive problem prevention, reduction of Non-productive Time (NPT) by well complications mitigation and simplification of tedious mud-logger labor. After carefully reviewing and studying the shortcomings of the recent techniques regarding cavings analysis, a conceptual design of automated cavings analysis technology is proposed in this thesis. The system is split into hardware and software parts. The first part includes circulation system for washing the cavings, as well as the camera and lightning facility. The camera is connected to the laptop with running software in the background, which is based on the Convolutional Neural Network (CNN). This algorithm analyzes the captured frames and delivers cavings’ shape, size and lithology as an output. Furthermore, feasibility study is conducted, in which rough costs of the proposed system are estimated.

AB - Analyzing return cuttings during drilling is one of the opportunities, besides core analysis, to observe and characterize the drilled rock. It gives real time information needed for bit depth correction and lithology correlation, such as rock type, color, texture (grain size, shape and sorting), cement amount, fossils presence, porosity and permeability. Correct measurements of those parameters (shape and size distribution in particular) improves the drilling performance and anticipates possible problems and complications. Cuttings and cavings presence in annular space increase the Equivalent Circulating Density (ECD), which leads to higher pressure losses; they are also one of the causes of Rate of Penetration (ROP) reduction because of chip hold down effect. Their shape is the inference for probable causes of borehole instability and quality of the mud cake. Several techniques have been used in last decades for obtaining the return cuttings parameters, such as their relative amount, particle size distribution (PSD), size and shape. They comprise state of the art technology based on computer vision techniques with machine learning algorithms as a software. A number of such techniques is already available on the market, and have their limitations and advantages. Basing on this principle, OMV is planning to build in house intelligent and cost-effective system which is capable of determining the cuttings parameters in real time. The built system should be feasible from the point of proactive problem prevention, reduction of Non-productive Time (NPT) by well complications mitigation and simplification of tedious mud-logger labor. After carefully reviewing and studying the shortcomings of the recent techniques regarding cavings analysis, a conceptual design of automated cavings analysis technology is proposed in this thesis. The system is split into hardware and software parts. The first part includes circulation system for washing the cavings, as well as the camera and lightning facility. The camera is connected to the laptop with running software in the background, which is based on the Convolutional Neural Network (CNN). This algorithm analyzes the captured frames and delivers cavings’ shape, size and lithology as an output. Furthermore, feasibility study is conducted, in which rough costs of the proposed system are estimated.

KW - drilling

KW - cuttings

KW - machine learning

KW - NPT

KW - neural network

KW - complication

KW - instability

KW - geomechanics

KW - computer vision

KW - Bohren

KW - Bohrschlamm

KW - maschinelles Lernen

KW - NPT

KW - neurales Netzwerk

KW - Komplikation

KW - Instabilität

KW - Geomechanik

KW - Computer Vision

M3 - Master's Thesis

ER -