TL;DR: In this paper, the authors used two separate back-propagation ANNs (BP-ANNs) to model porosity and permeability from well logs in the North Sea.
Abstract: Estimations of porosity and permeability from well logs are important yet difficult tasks encountered in geophysical formation evaluation and reservoir engineering. Motivated by recent results of artificial neural network (ANN) modelling offshore eastern Canada, we have developed neural nets for converting well logs in the North Sea to porosity and permeability. We use two separate back-propagation ANNs (BP-ANNs) to model porosity and permeability. The porosity ANN is a simple three-layer network using sonic, density and resistivity logs for input. The permeability ANN is slightly more complex with four inputs (density, gamma ray, neutron porosity and sonic) and more neurons in the hidden layer to account for the increased complexity in the relationships. The networks, initially developed for basin-scale problems, perform sufficiently accurately to meet normal requirements in reservoir engineering when applied to Jurassic reservoirs in the Viking Graben area. The mean difference between the predicted porosity and helium porosity from core plugs is less than 0.01 fractional units. For the permeability network a mean difference of approximately 400 mD is mainly due to minor core-log depth mismatch in the heterogeneous parts of the reservoir and lack of adequate overburden corrections to the core permeability. A major advantage is that no a priori knowledge of the rock material and pore fluids is required. Real-time conversion based on measurements while drilling (MWD) is thus an obvious application.
TL;DR: In this article, a detailed analysis of Ocean Drilling Program data from Amazon Fan was conducted to examine variables affecting compressional velocity in shallow, unconsolidated sediments, and three dominant variables (porosity, shale fraction, and consolidation history) were identified.
Abstract: Seeking a global empirical relationship between compressional wave velocity and porosity for siliciclastic sediments, we have brought together an extensive suite of both new and published log- and core-based data. We undertook a detailed statistical analysis of Ocean Drilling Program data from Amazon Fan to examine variables affecting compressional velocity in shallow, unconsolidated sediments. We identify three dominant variables (porosity, shale fraction, and consolidation history) and present two empirically determined boundary curves (one for normally consolidated sediments and a second for highly consolidated environments (e.g., accretionary prisms)). These two empirical relationships predict the compressional velocity of siliciclastic sedimentary rocks with water-filled pores as a function of porosity and clay content for the full range of observed porosities. Velocities of siliciclastic sedimentary rocks decrease rapidly with both increasing porosity and increasing clay content. At fractional porosities higher than about 0.4, fluid dominates the elastic properties, and velocity exhibits a subtle dependence on porosity. Remarkably, the Amazon Fan data show that clay content has no direct influence on velocity at high porosities. Both clay content and sorting do indirectly affect velocity, through their control of porosity. Burial affects velocity not only by compaction-related porosity decrease but also by pressure-induced increase of intergrain coupling. Because of the sensitivity of velocity to consolidation history, particularly at intermediate fractional porosities of about 0.30–0.40, no single velocity-porosity relationship can apply to all high-porosity sediments. The two proposed relationships fit the majority of published and new data. They are applicable, however, only for normally pressured, in situ conditions and water-filled pores.
TL;DR: In this article, the acoustic impedances of massive sulfides can be predicted from the physical properties (Vp, density) and modal abundances of common sulfide minerals using simple mixing relations.
Abstract: Laboratory studies show that the acoustic impedances of massive sulfides can be predicted from the physical properties (Vp, density) and modal abundances of common sulfide minerals using simple mixing relations. Most sulfides have significantly higher impedances than silicate rocks, implying that seismic reflection techniques can be used directly for base metals exploration, provided the deposits meet the geometric constraints required for detection. To test this concept, a series of 1-, 2-, and 3-D seismic experiments were conducted to image known ore bodies in central and eastern Canada. In one recent test, conducted at the Halfmile Lake coppernickel deposit in the Bathurst camp, laboratory measurements on representative samples of ore and country rock demonstrated that the ores should make strong reflectors at the site, while velocity and density logging confirmed that these reflectors should persist at formation scales. These predictions have been confirmed by the detection of strong reflections from the deposit using vertical seismic profiling and 2-D multichannel seismic imaging techniques.
TL;DR: In this article, a new empirical relationship between density and thermal conductivity for igneous rocks has been found, based on the rock forming processes which in turn generate typical mineral compositions.
TL;DR: In this article, a technique for determining the density of an earth formation with a logging sonde including a gamma ray source and two gamma ray detectors spaced at different distances from the source is disclosed.
Abstract: A technique for determining the density of an earth formation with a logging sonde including a gamma ray source and two gamma ray detectors spaced at different distances from the source is disclosed. The count rate of the short-spaced detector is measured in two energy ranges covering back-scattered gamma rays which have undergone relatively low and high attenuations respectively. A first density correction is determined from the difference between the apparent density derived from the count rate of the long-spaced detector and the density derived from the count rate of the short-spaced detector in the energy range covering gamma rays with relatively low attenuation. A second density correction is determined from the difference between the densities derived from the two short-spaced detectors' count rates. These two density corrections are added to the apparent density to give the true formation density.