Petrophysical Properties Determination of Tight Gas Sands From NMR Data Using Artificial Neural Network

Abstract

Abstract Analysis of heterogeneous gas sand reservoirs is one of the most difficult problems. These reservoirs usually produce from multiple layers with different petrophysical properties. Therefore, using new well logging techniques like NMR or a combination of NMR and conventional open hole logs is essential for improved reservoir characterization. Nuclear magnetic resonance (NMR) logs differ from conventional neutron, density, sonic and resistivity logs because the NMR measurements provide mainly lithology independent detailed porosity and offer a good evaluation of the hydrocarbon potential. NMR logs can also be used to determine formation permeability and capillary pressure. This paper focuses on permeability estimation from NMR logging data. Three models have been used to derive permeability from NMR; Kenyon model, Coates-Timer model and Bulk Gas Magnetic Resonance model. These models have advantages and limitations which depend on the reservoir characteristics. We first estimated permeability from NMR data using the Bulk Gas model. Then neural network model was developed to predict formation permeability using NMR and other open hole logs data. The permeability results of the neural network model and the Bulk Gas model were validated by core permeability for the studied wells. 1. Introduction Permeability is a measure of fluid rock conductivity. To be permeable, a rock must have interconnected porosity. Greater porosity usually corresponds to greater permeability; however, this is not always the case. Formation permeability is influenced by pore size, shape and continuity, as well as the amount of porosity. Permeability can be determined from resistivity gradients, permeability models based on porosity (f) and irreducible water saturation (Swi), formation tester (FT), and nuclear magnetic resonance (NMR). Perhaps, the most important feature of NMR logging is the ability to record a real-time permeability log. The potential benefits of NMR to oil companies are enormous. Log permeability measurements enable production rates prediction and allow optimization of production completion and programs stimulation while decreasing the cost of coring and testing wells especially in heterogeneous tight reservoirs where there is considerable permeability anisotropy. The field of interest is a gas condensate field producing from a Lower-Mesozoic reservoir. The reservoir is classified as a tight heterogeneous gas shaly sand reservoir. It suffers from lateral and vertical heterogeneity due to diagenesis effect (Kaolinite & Illite) and variation in grain size distribution. The petrophysical analysis indicates a narrow 8–12% porosity range, and a wide permeability range from 0.01 to 100 md. Fig. 1 shows core porositypermeability crossplot over whole reservoir section including all facies in different wells. The core data shows cloud of points with undefined trend, so, it is subdivided into six subunits, Oraby et. al.1, Hamada et. al.2.