Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations: Part 1 — Methodology

Geophysics ◽  
2010 ◽  
Vol 75 (2) ◽  
pp. R21-R35 ◽  
Author(s):  
Marit Ulvmoen ◽  
Henning Omre
Keyword(s):  
Seismic Data ◽  
Markov Chain Model ◽  
Complete Solution ◽  
Seismic Signal ◽  
Chain Model ◽  
Prior Model ◽  
Spatial Coupling ◽  
Spatial Coverage ◽  
Convolution Model ◽  
Likelihood Model

The focus of our study is lithology/fluid inversion with spatial coupling from prestack seismic amplitude variation with offset (AVO) data and well observations. The inversion is defined in a Bayesian setting where the complete solution is the posterior model. The prior model for the lithology/fluid (LF) characteristics is defined as a profile Markov random-field model with lateral continuity. Each vertical profile is further given as an inhomogeneous Markov-chain model upward through the reservoir. The likelihood model is defined by profile, and it relates the LF characteristics to the seismic data via a set of elastic material parameters and a convolution model. The likelihood model is approximated. The resulting approximate posterior model is explored using an efficient block Gibbs simulation algorithm. The inversion approach is evaluated on a synthetic realistic 2D reservoir. Seismic AVO data and well observations are integrated in a consistent manner to obtain predictions of the LF characteristics with associated uncertainty statements. The predictions appear very reliable despite the approximation of the posterior model, and errors in seismic data are the major contributions to the uncertainty. Resolution of the inversion is improved considerably by using a spatially coupled prior LF model, and LF units of [Formula: see text] thick can be identified even with a seismic signal-to-noise ratio of two. The inversion results appear robust toward varying model parameter values in the prior model as a result of the discretization of LF characteristics and seismic data with good spatial coverage.

Hierarchical Bayesian lithology/fluid prediction: A North Sea case study

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. B69-B85 ◽  
Author(s):  
Kjartan Rimstad ◽  
Per Avseth ◽  
Henning Omre
Keyword(s):  
North Sea ◽  
Rock Physics ◽  
Model Parameters ◽  
Gas Oil ◽  
Clear Fluid ◽  
Prior Model ◽  
Spatial Coupling ◽  
The North ◽  
Likelihood Model ◽  
Physics Model

Seismic 3D amplitude variation with offset (AVO) data from the Alvheim field in the North Sea are inverted into lithology/fluid classes, elastic properties, and porosity. Lithology/fluid maps over hydrocarbon prospects provide more reliable estimates of gas/oil volumes and improve the decision concerning further reservoir assessments. The Alvheim field is of turbidite origin with complex sand-lobe geometry and appears without clear fluid contacts across the field. The inversion is phrased in a Bayesian setting. The likelihood model contains a convolutional, linearized seismic model and a rock-physics model that capture vertical trends due to increased sand compaction and possible cementation. The likelihood model contains several global model parameters that are considered to be stochastic to adapt the model to the field under study and to include model uncertainty in the uncertainty assessments. The prior model on the lithology/fluid classes is a Markov random field that captures local vertical/horizontal continuity and vertical sorting of fluids. The predictions based on the posterior model are validated by observations in five wells used as blind tests. Hydrocarbon volumes with reliable gas/oil distributions are predicted. The spatial coupling provided by the prior model is crucial for reliable predictions; without the coupling, hydrocarbon volumes are severely underestimated. Depth trends in the rock-physics likelihood model improve the gas versus oil predictions. The porosity predictions reproduce contrasts observed in the wells, and mean square error is reduced by one-third compared to Gauss-linear predictions.

Bayesian lithology/fluid prediction and simulation on the basis of a Markov-chain prior model

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. R69-R78 ◽  
Author(s):  
Anne Louise Larsen ◽  
Marit Ulvmoen ◽  
Henning Omre ◽  
Arild Buland
Keyword(s):  
Markov Chain ◽  
Seismic Data ◽  
Recursive Algorithm ◽  
Noise Model ◽  
Prior Model ◽  
Likelihood Model ◽  
Band Limited ◽  
Prediction And Simulation ◽  
The Impact ◽  
Vertical Correlation

A technique for lithology/fluid (LF) prediction and simulation from prestack seismic data is developed in a Bayesian framework. The objective is to determine the LF classes along 1D profiles through a reservoir target zone. A stationary Markov-chain prior model is used to model vertical continuity of LF classes along the profile. The likelihood relates the LF classes to the elastic properties and to the seismic data, and it introduces vertical correlation because the seismic data are band-limited. An approximation of the likelihood model provides an approximate posterior model that is a Markov chain. The approximate posterior can be assessed by an exact and efficient recursive algorithm. The LF inversion approach is evaluated on a synthetic 1D profile that is inspired by a North Sea sandstone reservoir. With a realistic wavelet-colored noise model and a S/N ratio of three in the seismic data, the results are reliable. The LF classes and the interfaces between zones are largely correct. The prediction uncertainty increases if the number of zones increases and zone thicknesses decreases. The study clearly demonstrates the impact of a vertically coupled prior Markov model for the LF classes.

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Performance Analysis of Synchronous Multi-Radio Multi-Link MAC Protocols in IEEE 802.11be Extremely High Throughput WLANs

2020 ◽  
Vol 11 (1) ◽  
pp. 317
Author(s):  
Taewon Song ◽  
Taeyoon Kim
Keyword(s):  
Ieee 802.11 ◽  
Markov Chain Model ◽  
Careful Consideration ◽  
Mac Protocols ◽  
Media Access Control ◽  
Chain Model ◽  
Distributed Coordination ◽  
Media Access ◽  
Medium Access ◽  
Coordination Function

The representative media access control (MAC) mechanism of IEEE 802.11 is a distributed coordination function (DCF), which operates based on carrier-sense multiple access with collision avoidance (CSMA/CA) with binary exponential backoff. The next amendment of IEEE 802.11 being developed for future Wi-Fi by the task group-be is called IEEE 802.11be, where the multi-link operation is mainly discussed when it comes to MAC layer operation. The multi-link operation discussed in IEEE 802.11be allows multi-link devices to establish multiple links and operate them simultaneously. Since the medium access on a link may affect the other links, and the conventional MAC mechanism has just taken account of a single link, the DCF should be used after careful consideration for multi-link operation. In this paper, we summarize the DCFs being reviewed to support the multi-radio multi-link operation in IEEE 802.11be and analyze their performance using the Markov chain model. Throughout the extensive performance evaluation, we summarize each MAC protocol’s pros and cons and discuss essential findings of the candidate MAC protocols.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

Sign in / Sign up

Export Citation Format

Share Document