In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

Flow unit and reservoir rock type identification in carbonates are difficult due to the intricacy of pore networks caused by facies changes and diagenetic processes. On the other hand, these classifications of rock type are necessary for understanding a reservoir and predicting its production performance in the face of any activity. The current study focuses on rock type and flow unit classification for the Mishrif reservoir in Iraq's southeast and the study is based on data from five wells that penetrate it. Integration of several methods was used to determine the flow unit based on well log interpretation and petrophysical properties. The flow units were identified using the Quality Index of Rock and the Indicator of Flow Zone. Th

Reservoir rock typing integrates geological, petrophysical, seismic, and reservoir data to identify zones with similar storage and flow capacities. Therefore, three different methods to determine the type of reservoir rocks in the Mushrif Formation of the Amara oil field. The first method represents cluster analysis, a statistical method that classifies data points based on effective porosity, clay volume, and sonic transient time from well logs or core samples. The second method is the electrical rock type, which classifies reservoir rocks based on electrical resistivity. The permeability of rock types varies due to differences in pore geometry, mineral composition, and fluid saturation. Resistivity data are usually obtained from w

   The Compressional-wave (Vp) data are useful for reservoir exploration, drilling operations, stimulation, hydraulic fracturing employment, and development plans for a specific reservoir. Due to the different nature and behavior of the influencing parameters, more complex nonlinearity exists for Vp modeling purposes. In this study, a statistical relationship between compressional wave velocity and petrophysical parameters was developed from wireline log data for Jeribe formation in Fauqi oil field south Est Iraq, which is studied using single and multiple linear regressions. The model concentrated on predicting compressional wave velocity from petrophysical parameters and any pair of shear waves velocity, porosity, density, and

   The Compressional-wave (Vp) data are useful for reservoir exploration, drilling operations, stimulation, hydraulic fracturing employment, and development plans for a specific reservoir. Due to the different nature and behavior of the influencing parameters, more complex nonlinearity exists for Vp modeling purposes. In this study, a statistical relationship between compressional wave velocity and petrophysical parameters was developed from wireline log data for Jeribe formation in Fauqi oil field south Est Iraq, which is studied using single and multiple linear regressions. The model concentrated on predicting compressional wave velocity from petrophysical parameters and any pair of shear waves velocity, porosity, density, a

Nasryia oil field is located about 38 Km to the north-west of Nasryia city. The field was discovered in 1975 after doing seismic by Iraqi national oil company. Mishrif formation is a carbonate rock (Limestone and Dolomite) and its thickness reach to 170m. The main reservoir is the lower Mishrif (MB) layer which has medium permeability (3.5-100) md and good porosity (10-25) %. Form well logging interpretation, it has been confirmed the rock type of Mishrif formation as carbonate rock. A ten meter shale layer is separating the MA from MB layer. Environmental corrections had been applied on well logs to use the corrected one in the analysis. The combination of Neutron-Density porosity has been chosen for interpretation as it is c

A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g