Velocity and density measured in a well are crucial for synthetic seismic generation which is, in turn, a key to interpreting real seismic amplitude in terms of lithology, porosity and fluid content. Investigations made in the water wells usually consist of spontaneous potential, resistivity long and short normal, point resistivity and gamma ray logs. The sonic logs are not available because these are usually run in the wells drilled for hydrocarbons. To generate the synthetic seismograms, sonic and density logs are required, which are useful to precisely mark the lithology contacts and formation tops. An attempt has been made to interpret the subsurface soil of the aquifer system by means of resistivity to seismic inversion. For this purpose, resistivity logs and surface resistivity sounding were used and the resistivity logs were converted to sonic logs whereas surface resistivity sounding data transformed into seismic curves. The converted sonic logs and the surface seismic curves were then used to generate synthetic seismograms. With the utilization of these synthetic seismograms, pseudo-seismic sections have been developed. Subsurface lithologies encountered in wells exhibit different velocities and densities. The reflection patterns were marked by using amplitude standout, character and coherence. These pseudo-seismic sections were later tied to well synthetics and lithologs. In this way, a lithology section was created for the alluvial fill. The cross-section suggested that the eastern portion of the studied area mainly consisted of sandy fill and the western portion constituted clayey part. This can be attributed to the depositional environment by the Indus and the Kabul Rivers.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10661-011-1955-4DOI Listing

Publication Analysis

Top Keywords

sonic logs
12
synthetic seismograms
12
logs surface
12
aquifer system
8
generate synthetic
8
resistivity logs
8
surface resistivity
8
resistivity sounding
8
converted sonic
8
seismic curves
8

Similar Publications

Comprehensive input models and machine learning methods to improve permeability prediction.

Sci Rep

September 2024

Earth Sciences Department, Faculty of Natural Science, University of Tabriz, Tabriz, Iran.

This study investigates the use of machine learning techniques and the proper selection of input data to estimate permeability in geosciences, using six types of input logs: gamma ray (GR), resistivity (RT), effective porosity (PHIE), density (RHO), sonic (DT), and compensated neutron porosity (NPHI). A total of 57 models were constructed using combinations of these logs and tested using five machine learning methods: Extreme Learning Machine (ELM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP). This approach produced 285 unique permeability predictions.

View Article and Find Full Text PDF

The Upper Triassic Kurra Chine Formation in the Sarta oil field of the Kurdistan Region of Northern Iraq has garnered limited attention, notwithstanding the keen interest of numerous international oil companies in drilling wells within this geological epoch. This study endeavors to thoroughly investigate the Formation Evaluation and petrophysical properties of the Kurra Chine Formation in the production oil field, with a specific focus on Sarta Well-2 (S-2). The research incorporates diverse methods for formation evaluation and analysis of petrophysical properties, employing conventional wireline logs such as Gamm-Ray, Neutron, Density, Sonic, Resistivity, Caliper, and Bit size.

View Article and Find Full Text PDF

Understanding the spatial variation in lithology is crucial for characterizing reservoirs, as it governs the distribution of petrophysical characteristics. This study focuses on predicting the lithology of carbonate rocks (limestone, argillaceous limestone, marly limestone, and marl) within the Kometan Formation, Khabbaz Oil Field, Northern Iraq, using well logs. Precise lithology prediction was achieved by applying multivariate regression method on neutron, sonic, and density logs.

View Article and Find Full Text PDF

Baltim Eastern and Northern gas fields in the offshore Nile Delta have very high gas condensate accumulations. Therefore, the present research evaluates Abu Madi and Qawasim Formations and defines the petrophysical parameters for them using various data from five wells composed of wireline logs (gamma-ray, density, neutron, sonic, resistivity), core data, pressure data, and cross-plots. In the current study, the formations of the main reservoirs were evaluated qualitatively and quantitatively based on the petrophysical analysis to assess the production potential.

View Article and Find Full Text PDF

Sonic logs are essential for determining important reservoir properties such as porosity, permeability, lithology, and elastic properties, among others, and yet may be missing in some well logging suites due to high acquisition costs, borehole washout, tool damage, poor tool calibration, or faulty logging instruments. This study aims at predicting the compressional sonic log from commonly acquired logs (gamma ray, resistivity, density, and neutron-porosity) in the Tano basin of Ghana using Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) Machine Learning (ML) algorithms and comparing the performances of the algorithms. The algorithms were trained with 70% of the data from two wells and tested using the remaining 30% of the data from the wells after cross-validation.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!