Improved Typelog Alignment for Automated Geosteering Using Multi-Stage Penalized Optimization

2021 ◽  
Author(s):  
Jin Sun ◽  
Stefan Maus
Keyword(s):  
Prior Distribution ◽  
Alignment Method ◽  
Directional Drilling ◽  
Combine Data ◽  
Multi Stage ◽  
Drilling Data ◽  
Geological Formation ◽  
The Subject ◽  
Logging While Drilling ◽  
Penalized Optimization

Abstract Automated geosteering relies on logging-while-drilling data from offset wells to make inferences about the geological formation and help guide directional drilling of the subject well. When data from multiple offset wells are available, it is desirable to consistently combine data typelogs from these wells to better estimate the 3D geological formation around the drilling path. We develop a quantitative typelog alignment method based on a Bayesian approach, where the alignment map between pairs of typelogs are modeled as a random function with a prior distribution. A multi-stage penalized procedure is developed that optimizes this alignment map to minimize a misfit function, while taking the prior knowledge into consideration.

Deep Multi-Stage Approach For Emotional Body Gesture Recognition In Job Interview

2021 ◽  
Author(s):  
Intissar Khalifa ◽  
Ridha Ejbali ◽  
Raimondo Schettini ◽  
Mourad Zaied
Keyword(s):  
Gesture Recognition ◽  
Affective Computing ◽  
Psychological State ◽  
Sources Of Information ◽  
Emotion Classification ◽  
Convolutional Network ◽  
Job Interview ◽  
Multi Stage ◽  
Representation Technique ◽  
The Subject

Abstract Affective computing is a key research topic in artificial intelligence which is applied to psychology and machines. It consists of the estimation and measurement of human emotions. A person’s body language is one of the most significant sources of information during job interview, and it reflects a deep psychological state that is often missing from other data sources. In our work, we combine two tasks of pose estimation and emotion classification for emotional body gesture recognition to propose a deep multi-stage architecture that is able to deal with both tasks. Our deep pose decoding method detects and tracks the candidate’s skeleton in a video using a combination of depthwise convolutional network and detection-based method for 2D pose reconstruction. Moreover, we propose a representation technique based on the superposition of skeletons to generate for each video sequence a single image synthesizing the different poses of the subject. We call this image: ‘history pose image’, and it is used as input to the convolutional neural network model based on the Visual Geometry Group architecture. We demonstrate the effectiveness of our method in comparison with other methods in the state of the art on the standard Common Object in Context keypoint dataset and Face and Body gesture video database.

scholarly journals Prediction of reservoir depletion degree and production GOR using logging-while-drilling data

2009 ◽  
Vol 36 (5) ◽  
pp. 617-622 ◽  
Author(s):  
Li Fangming ◽  
Tian Zhongyuan ◽  
Jiang Aming ◽  
Wang Xiaoxia
Keyword(s):  
Drilling Data ◽  
Logging While Drilling

An Introduction to Dynamic Programming

1961 ◽  
Vol 16 (04) ◽  
pp. 261-274
Author(s):  
Brian Gluss
Keyword(s):  
Dynamic Programming ◽  
Small Body ◽  
Decision Processes ◽  
Theory And Practice ◽  
Rand Corporation ◽  
The Past ◽  
Multi Stage ◽  
The Subject ◽  
New Research ◽  
General Mathematical Theory

Dynamic programming, a mathematical field that has grown up in the past few years, is recognized in the U.S.A. as an important new research tool. However, in other countries, little interest has as yet been taken in the subject, nor has much research been performed. The objective of this paper is to give an expository introduction to the field, and give an indication of the variety of actual and possible areas of application, including actuarial theory.In the last decade a large amount of research has been performed by a small body of mathematicians, most of them members of the staff of the RAND Corporation, in the field of multi-stage decision processes, and during this time the theory and practice of the art have experienced great advances. The leading force in these advances has been Richard Bellman, whose contributions to the subject, which he has entitledDynamic Programming[1], have had effects not only in immediate fields of application but also in general mathematical theory; for example, the calculus of variations (see chapter IX of [1]), and linear programming (chapter VI).

DPCM Compression for Real-Time Logging While Drilling Data

2010 ◽  
Vol 5 (3) ◽  
Author(s):  
Yu Zhang ◽  
Sheng-hui Wang ◽  
Ke Xiong ◽  
Zheng-ding Qiu ◽  
Dong-mei Sun
Keyword(s):  
Real Time ◽  
Drilling Data ◽  
Logging While Drilling

REPLY BY AUTHORS TO DISCUSSION BY W. T. HIGDON

Geophysics ◽  
1963 ◽  
Vol 28 (2) ◽  
pp. 310-313 ◽  
Author(s):  
V. V. Jagannadha Sarma ◽  
V. Bhaskara Rao
Keyword(s):  
Electrical Resistivity ◽  
The Other ◽  
Grain Sizes ◽  
Introductory Statement ◽  
Three Samples ◽  
Wide Range ◽  
Geological Formation ◽  
The Subject ◽  
Two Parameters ◽  
Broad Generalization

The introductory statement that “The electrical resistivity of a geological formation is a function of … (1) the amount of moisture and consequently of porosity; (2) salinity of the moisture; and (3) grain size of the formation” is intended as a broad generalization to include possible parameters affecting resistivity variations. Of these three parameters, only the influence of the amount of moisture on the electrical resistivity variations is the subject of the results reported. At the same time, the possible effects of the other two parameters have been taken into consideration in the control of experiments and discussion of results. Thus, at least three samples (2, 3, and 4) of known average grain sizes of 1.5, 0.75, and 0.37 mm are treated with five samples of water with a wide range of known salinities. By such a distribution, it is ensured that the electrical resistivity variations of the sample in a given run are due only to the varying water content. Corrections to the data required for representation are thus avoided.

3D electromagnetic inversion of logging while drilling data

2017 ◽  
Author(s):  
Dikun Yang ◽  
David Marchant ◽  
Eldad Haber
Keyword(s):  
Drilling Data ◽  
Logging While Drilling
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