Application of inverse filtering to gamma‐ray logs: A case study

Geophysics ◽  
1984 ◽  
Vol 49 (8) ◽  
pp. 1369-1373 ◽  
Author(s):  
Q. Bristow ◽  
J. G. Conaway ◽  
P. G. Killeen
Keyword(s):  
Seismic Data ◽  
Gamma Ray ◽  
Inverse Filtering ◽  
Data Set ◽  
Natural Gamma ◽  
On Line ◽  
Computer Based ◽  
Filter Approach ◽  
Deconvolution Techniques

The application of digital inverse filter deconvolution techniques to seismic data has been routine for many years. More recently these techniques have been extended to natural gamma‐ray logging in order to improve the spatial resolution of the recorded logs (Czubek, 1971; Conaway and Killeen, 1978a,b). Early work in this field (Scott, 1963) involved an iterative procedure which required repeated processing of an entire log data set. Such a technique does not lend itself to continuous on‐line deconvolution of a log while the logging operation is in progress. The inverse digital filter approach, by contrast, is particularly well suited for implementation in a computer‐based borehole logging data acquisition system. Such a system had been developed at the Geological Survey of Canada (G.S.C.) by 1978 and was described by Bristow and Killeen (1978) and Bristow (1977, 1979).

Applications of Artificial Neural Networks for Seismic Facies Classification

2021 ◽  
Vol 73 (02) ◽  
pp. 68-69
Author(s):  
Chris Carpenter
Keyword(s):  
Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Probabilistic Neural Network ◽  
Oil Field ◽  
Seismic Facies ◽  
Data Set ◽  
Full Field ◽  
Facies Classification

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200577, “Applications of Artificial Neural Networks for Seismic Facies Classification: A Case Study From the Mid-Cretaceous Reservoir in a Supergiant Oil Field,” by Ali Al-Ali, Karl Stephen, SPE, and Asghar Shams, Heriot-Watt University, prepared for the 2020 SPE Europec featured at the 82nd EAGE Conference and Exhibition, originally scheduled to be held in Amsterdam, 1-3 December. The paper has not been peer reviewed. Facies classification using data from sources such as wells and outcrops cannot capture all reservoir characterization in the interwell region. Therefore, as an alternative approach, seismic facies classification schemes are applied to reduce the uncertainties in the reservoir model. In this study, a machine-learning neural network was introduced to predict the lithology required for building a full-field Earth model for carbonate reservoirs in southern Iraq. The work and the methodology provide a significant improvement in facies classification and reveal the capability of a probabilistic neural network technique. Introduction The use of machine learning in seismic facies classification has increased gradually during the past decade in the interpretation of 3D and 4D seismic volumes and reservoir characterization work flows. The complete paper provides a literature review regarding this topic. Previously, seismic reservoir characterization has revealed the heterogeneity of the Mishrif reservoir and its distribution in terms of the pore system and the structural model. However, the main objective of this work is to classify and predict the heterogeneous facies of the carbonate Mishrif reservoir in a giant oil field using a multilayer feed-forward network (MLFN) and a probabilistic neural network (PNN) in nonlinear facies classification techniques. A related objective was to find any domain-specific causal relationships among input and output variables. These two methods have been applied to classify and predict the presence of different facies in Mishrif reservoir rock types. Case Study Reservoir and Data Set Description. The West Qurna field is a giant, multibillion-barrel oil field in the southern Mesopotamian Basin with multiple carbonate and clastic reservoirs. The overall structure of the field is a north/south trending anticline steep on the western flank and gentle on the eastern flank. Many producing reservoirs developed in this oil field; however, the Mid- Cretaceous Mishrif reservoir is the main producing reservoir. The reservoir consists of thick carbonate strata (roughly 250 m) deposited on a shallow water platform adjacent to more-distal, deeper-water nonreservoir carbonate facies developing into three stratigraphic sequence units in the second order. Mishrif facies are characterized by a porosity greater than 20% and large permeability contrast from grainstones to microporosity (10-1000 md). The first full-field 3D seismic data set was achieved over 500 km2 during 2012 and 2013 in order to plan the development of all field reservoirs. A de-tailed description of the reservoir has been determined from well logs and core and seismic data. This study is mainly based on facies log (22 wells) and high-resolution 3D seismic volume to generate seismic attributes as the input data for the training of the neural network model. The model is used to evaluate lithofacies in wells without core data but with appropriate facies logs. Also, testing was carried out in parallel with the core data to verify the results of facies classification.

A statistical methodology for deriving reservoir properties from seismic data

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1437-1450 ◽  
Author(s):  
Frédérique Fournier ◽  
Jean‐François Derain
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Reservoir Properties ◽  
Data Set ◽  
Analysis Technique ◽  
Seismic Facies Analysis ◽  
Statistical Relationships

The use of seismic data to better constrain the reservoir model between wells has become an important goal for seismic interpretation. We propose a methodology for deriving soft geologic information from seismic data and discuss its application through a case study in offshore Congo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing the traces at the reservoir level. We built statistical relationships between seismic attributes and reservoir properties from a calibration population consisting of wells and their adjacent traces. The correlation studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression between the canonical seismic variables and the reservoir properties, whenever they are predictable. In the case study, we predicted estimates and associated uncertainties on the lithofacies thicknesses cumulated over the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological prediction results in the cases of a calibration on the whole data set and a calibration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly because the large scale geological variations (and associated seismic ones) over the field can be accounted for.

Model-based redatuming of seismic data: An inverse-filter approach

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. Q1-Q13 ◽  
Author(s):  
Thomas Planès ◽  
Roel Snieder ◽  
Satyan Singh
Keyword(s):  
Standard Model ◽  
Nondestructive Testing ◽  
Seismic Data ◽  
Inverse Filter ◽  
Data Set ◽  
Model Based ◽  
Filter Approach ◽  
Internal Multiples

Standard model-based redatuming techniques allow focusing of the direct waves at the new datum, but the focus can be degraded because of surface multiples and internal multiples in the overburden. We demonstrate that if the medium above the redatuming level is known, these multiples can be correctly handled. We compute the exact focusing functions, free of multiples, using an inverse-filter approach. These focusing functions create downward-radiating and upward-radiating virtual sources at the new datum. The surface responses to these virtual sources are then used to compute the objective redatumed data set through multidimensional deconvolution. The redatumed data set corresponds to a virtual acquisition made at the new datum and for which the imprint of the overburden is completely removed. We test the technique on 2D acoustic synthetic examples corresponding to a seismic context and an acoustic nondestructive testing context.

An integrated surface and borehole seismic case study: Fort St. John Graben area, Alberta, Canada

Geophysics ◽  
1993 ◽  
Vol 58 (11) ◽  
pp. 1662-1675
Author(s):  
Ronald C. Hinds ◽  
Richard Kuzmiski ◽  
Neil L. Anderson ◽  
Barry R. Richards
Keyword(s):  
Seismic Data ◽  
Seismic Profile ◽  
Well Log ◽  
Data Set ◽  
Seismic Control ◽  
Subsurface Geology ◽  
Vsp Data ◽  
Borehole Seismic ◽  
Sandstone Facies

The deltaic sandstones of the basal Kiskatinaw Formation (Stoddard Group, upper Mississippian) were preferentially deposited within structural lows in a regime characterized by faulting and structural subsidence. In the Fort St. John Graben area, northwest Alberta, Canada, these sandstone facies can form reservoirs where they are laterally sealed against the flanks of upthrown fault blocks. Exploration for basal Kiskatinaw reservoirs generally entails the acquisition and interpretation of surface seismic data prior to drilling. These data are used to map the grabens in which these sandstones were deposited, and the horst blocks which act as lateral seals. Subsequent to drilling, vertical seismic profile (VSP) surveys can be run. These data supplement the surface seismic and well log control in that: 1) VSP data can be directly correlated to surface seismic data. As a result, the surface seismic control can be accurately tied to the subsurface geology; 2) Multiples, identified on VSP data, can be deconvolved out of the surface seismic data; and 3) The subsurface, in the vicinity of the borehole, is more clearly resolved on the VSP data than on surface seismic control. On the Fort St. John Graben data set incorporated into this paper, faults which are not well resolved on the surface seismic data, are better delineated on VSP data. The interpretive processing of these data illustrate the use of the seismic profiling technique in the search for hydrocarbons in structurally complex areas.

scholarly journals Imaging subtle faults using azimuthal coherence attribute: A case study from Central Saudi Arabia

GeoArabia ◽  
2012 ◽  
Vol 17 (4) ◽  
pp. 43-54
Author(s):  
Faisal Alqahtani ◽  
Abdullatif A. Al-Shuhail
Keyword(s):  
Saudi Arabia ◽  
Seismic Data ◽  
Seismic Waves ◽  
Detection Method ◽  
Gas Field ◽  
Data Set ◽  
Attribute Analysis ◽  
Edge Detection Method ◽  
Pass Through

ABSTRACT The coherence attribute is an edge detection method that is widely used for interpreting faults on 3-D seismic time slices. The traditional coherence attribute is calculated on migrated volumes using traces from all available azimuths. It has recently been shown that calculating coherence along specific azimuths can enhance the detection of faults running perpendicular to those azimuths. In this study, we applied azimuthal coherence attribute analysis on a 3-D seismic data set from a gas field in Central Saudi Arabia. We generated four migrated 3-D data volumes sorted by azimuth in addition to a conventional full-azimuth volume. We then calculated the coherence attribute for all volumes and compared each azimuthal coherence volume to the conventional full-azimuth coherence volume. The azimuthal coherence results exhibited an improved definition for faults whose strikes are perpendicular to the sorting azimuth. More specifically, systems of NW-trending discontinuities were imaged more clearly in the NE-SW oriented coherence volume than it was in the full-azimuth coherence volume. The reason for this enhancement is the fact that seismic waves tend to avoid passing through the fault when they propagate parallel to the fault strike therefore missing the effects of the fault while they must pass through the fault when propagating perpendicular to the fault strike which results in better illumination of the fault.

scholarly journals Acoustic Log Prediction on the Basis of Kernel Extreme Learning Machine for Wells in GJH Survey, Erdos Basin

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Jianhua Cao ◽  
Yancui Shi ◽  
Dan Wang ◽  
Xiankun Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Gamma Ray ◽  
Fluid Pressure ◽  
Petroleum Exploration ◽  
Velocity Inversion ◽  
Natural Gamma ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Set Up

In petroleum exploration, the acoustic log (DT) is popularly used as an estimator to calculate formation porosity, to carry out petrophysical studies, or to participate in geological analysis and research (e.g., to map abnormal pore-fluid pressure). But sometime it does not exist in those old wells drilled 20 years ago, either because of data loss or because of just being not recorded at that time. Thus synthesizing the DT log becomes the necessary task for the researchers. In this paper we propose using kernel extreme learning machine (KELM) to predict missing sonic (DT) logs when only common logs (e.g., natural gamma ray: GR, deep resistivity: REID, and bulk density: DEN) are available. The common logs are set as predictors and the DT log is the target. By using KELM, a prediction model is firstly created based on the experimental data and then confirmed and validated by blind-testing the results in wells containing both the predictors and the target (DT) values used in the supervised training. Finally the optimal model is set up as a predictor. A case study for wells in GJH survey from the Erdos Basin, about velocity inversion using the KELM-estimated DT values, is presented. The results are promising and encouraging.

Technology-Supported Constructivism

2011 ◽  
pp. 158-176
Author(s):  
Melodee Landis
Keyword(s):  
Learning Communities ◽  
Constructivist Learning ◽  
Management Tool ◽  
Face To Face ◽  
On Line ◽  
Computer Based ◽  
On Line Learning ◽  
Constructivist Approaches ◽  
Specific Technology

This chapter asserts that the processes used in on-line, computer-based distance learning are a natural fit for constructivist learning. Learners today may be too dependent on teacher-centered instruction; the independent problem-solving required of the learner in on-line learning may be more likely to stimulate a deeper grasp of concepts under study. This case study was conducted with five face-to-face meetings and an online course management tool. While the enrollment of seventy-two new teachers appeared unmanageable, the use of on-line learning communities helped to personalize the course. The chapter includes an overview of constructivist approaches to instruction, citing both classic and contemporary experts, and continues with a description of how technology was used to support constructivism. The chapter concludes with an analysis of specific technology tools that can enhance different stages of constructivist learning.

Compressive seismic data acquisition in a desert area of western China: A case study

2020 ◽  
Vol 39 (5) ◽  
pp. 340-344
Author(s):  
Guoxu Shu ◽  
Taikun Shi ◽  
Liang Huang ◽  
Zhenghui Gao ◽  
Gonghe Lv ◽  
...  
Keyword(s):  
Seismic Data ◽  
Western China ◽  
Seismic Exploration ◽  
Data Reconstruction ◽  
Desert Area ◽  
Data Set ◽  
Imaging Quality ◽  
Seismic Data Acquisition ◽  
Legacy Data

The seismic exploration industry continuously demands better imaging quality and consequently requires denser spatial sampling, which increases acquisition cost and time. To alleviate this burden, compressive sensing (CS) theory has been introduced in the design of acquisition geometry, as it requires fewer shot and receiver locations than traditional methods. In 2017, we conducted a field experiment in a desert area in western China. This was the first such test in China to utilize CS theory in a field test. The survey had 1760 shot records with irregular shot and receiver locations designed with guidance from CS theory. By way of data reconstruction, a seismic data set with higher sampling density (7.5 × 7.5 m bin size) was acquired, and the imaging quality was improved significantly compared to existing legacy data (15 × 15 m bin size). These results indicate that a CS-designed acquisition may reduce cost while enhancing imaging quality.

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