Resevoir Permeability Prediction Using Artificial Neural Network; a Case Study of "XZ" Field, Offshore Niger Delta

Simple item page
dc.contributor.authorOzebo, V.C
dc.contributor.authorEzimadu, C.C
dc.date.accessioned2016-10-14T09:04:23Z
dc.date.available2016-10-14T09:04:23Z
dc.date.issued2015-10-05
dc.descriptionConference Paperen_US
dc.description.abstractReservoir Permeability is one of the most important characteristics of hydrocarbon bearing formations. A good knowledge of a formation's permeability helps geophysicist to efficiently manage the production process. Formation permeability is often measured in the laboratory from cores or evaluated from well test data. Core analysis and well test data, however, can only be gotten from a few wells in a field due to economic factors, while majority of wells are logged.In this study, an artificial neural network has been designed with PETREL TM, which is able to predict permeability of a formation using the data gotten from geophysical well logs with good accuracy. A case study from XZ field offshore Niger Delta is presented. Five well log responses (Gamma Ray Log (GR), Deep Resistivity (RD), Formation Density (DEN), Neutron Porosity (PHIN) and Density Porosity (PHID))were initially used as inputs in the ANN to predict permeability. Core permeability from one of the wells (OS 1) was used as target data in the ANN to test the prediction. The accuracy of the ANN approach is tested by regression plots of predicted values of permeability with core permeability which is the standard. Excellent matching of core data and predicted values reflects the accuracy of the technique. Permeability estimations/predictions presented in this paper have a correlation coefficient of 0.8 where 1.0 is a perfect match. This work showed that prediction result is improved by adding core porosity in the training, carefully selecting input data and increasing the number of iterations reasonably.en_US
dc.identifier.citationOzebo, V.C and Ezimadu, C.C (2015) Resevoir Permeability Prediction Using Artificial Neural Network; a Case Study of "XZ" Field, Offshore Niger Delta. Being a Paper presented at the 38th Annual Conference of Nigerian Institute of Physics (NIP), held at the Olabisi Onabanjo University, Ago-Iwoye.en_US
dc.identifier.urihttp://ir.unilag.edu.ng:8080/xmlui/handle/123456789/1024
dc.language.isoenen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectReservoir permeabilityen_US
dc.titleResevoir Permeability Prediction Using Artificial Neural Network; a Case Study of "XZ" Field, Offshore Niger Deltaen_US
dc.typePresentationen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
RESERVOIR PERMEABILITY PREDICTION USING ARTIFICIAL NEUTRAL NETWORK., A CASE OF 'XZ' FIELD, OFFSHORE NIGER DELTA.pdf
Size:
7.95 MB
Format:
Adobe Portable Document Format
Description:
Full Papers
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Conference Papers