Evolution of Sandstone Porosity Through Time. The Modified Scherer Model: A Calculation Method Applicable to 1-D Maturity Modeling and Perhaps to Reservoir Prediction

Scherer's model of 1987 successfully predicted present-day porosities of sandstones using four factors: burial depth, stratigraphic age, quartz content, and degree of sorting. This model now has been modified to predict sandstone porosity through time in order to provide a better way to model sandstone porosity for 1-D basin modeling. The Modified Scherer Model (MSM) has many advantages over the depth-dependent Sclater-Christie (Athy) and Falvey-Middleton equations used for calculating porosity in 1-D modeling, because it predicts major differences resulting from age, sedimentation rate, sandstone composition, and pressure that are not considered in depth-dependent models. The only additional information required by the MSM is quartz content and Trask sorting coefficient. If these are not known, they may be estimated with reasonable confidence. The Modified Scherer Method could be added easily to existing software packages for 1-D modeling. It also may prove useful for making preliminary estimates of reservoir porosity (including timing of porosity loss), especially in early stages of exploration.     

The Amoco Cadiz oil spill. A summary of observations made by U.S. scientists 23 March–10 May, 1978

This paper summarizes briefly the observations made by a team of United States scientists during the two months immediately after the grounding of the Amoco Cadiz. Most of the information has been abstracted from the NOAA/EPA preliminary scientific report (Hess, 1978) which provides a more complete account of objectives and results to date. It is hoped that maximum use may be derived from a study of the effects of the spill.     

Petrologic consequences of rift propagation on oceanic spreading ridges

The production of anomalously differentiated lava compositions at several mid-ocean spreading centers can be attributed to magmatic processes associated with propagating rifts. The degree of differentiation attained by magmas beneath oceanic spreading ridges depends mainly on the balance between cooling rate and the supply rate of new magma to shallow chambers. Low supply rates and moderate cooling rates allow advanced degrees of closed-system fractionation to occur. High supply rates result in open systems in which magma compositions are buffered by frequent replenishment with new hot magma. Propagating rift tips are a special class of ridge-transform intersection in which the balance between cooling and supply rates is conducive to the development of advanced degrees of differentiation over an expanded length of ridge. This balance is affected by the spreading rate, the propagation rate of the rift, the length of the bounding transform and proximity to hotspots. Maximum compositional variability and maximum degree of differentiation occur within 50 km of propagating rift tips and subsequently diminish with increasing distance. Rifts that propagate through plates in directions approximating their absolute motion relative to the lower mantle are characterized by the presence of anomalously differentiated lavas over longer ridge segments than are rifts that propagate against their absolute motion. Geochemical anomalies may persist, though changing in degree and extent, for several million years on ridge segments that stop propagating. The concept of “magnetic telechemistry” is generally supported by our study, but in the vicinity of hotspots, magnetic anomaly amplitude may be controlled more by bathymetric and/or thickened magnetic layer effects than by geochemistry.     

Confidence regions for fabric shape diagrams

Fabric shape is often quantified using the three eigenvalues from the ‘orientation tensor’ method applied to a sample of directions. Several studies have used eigenvalues plotted on fabric shape diagrams to distinguish sedimentary facies or strain histories. However, such studies seldom consider how well the sample eigenvalues represent the true fabric shape. In this paper, we use ‘bootstrapping’ techniques to define confidence regions for sample eigenvalues, and show that sample and population eigenvalues may differ substantially. Confidence regions are often very large for small sample sizes, and we recommend that sample sizes should be at least 50.