Predicting microporosity and permeability in clastic reservoirs is a challenge in reservoir quality assessment, especially in formations where direct measurements are difficult or expensive. These reservoir properties are fundamental in determining a reser voir's capacity for fluid storage and transmission, yet conventional methods for evaluating them, such as Mercury Injection Capillary Pressure (MICP) and Scanning Electron Microscopy (SEM), are resource - intensive. The aim of this study is to develop a cost - effective machine learning model to predict complex reservoir properties using readily available field data and basic laboratory analyses. A Random Forest classifier was employed, utilizing key geological parameters such as porosity, grain size distri bution, and spectral gamma - ray (SGR) measurements. An uncertainty analysis was applied to account for natural variability, expanding the dataset, and enhancing the model's robustness. The model achieved a high level of accuracy in predicting microporosity (93%) and permeability levels (88%). By using easily obtainable data, this model reduces the reliance on expensive laboratory methods, making it a valuable tool for early - stage exploration, especially in remote or offshore environments. The integration of machine learning with uncertainty analysis provides a reliable and cost - effective approach for evaluating key reservoir properties in siliciclastic formations. This model offers a practical solution to improve reservoir quality assessments, enabling more i nformed decision - making and optimizing exploration efforts.

There is another and perhaps more profound Point. The nature of a trail is that it divides space into two sets, success (being on the trail) and failure (not being so). If you are in tne success set, you are near other points of success: that means success is correlateri.

A. YONEZAWA and C. HEWITT 41 REPRESENTATIONS FOR ABSTRACT REASONING 4 A maximal method for set variables in automatic theorem-proving W. W. BLEDSOE

B.RANDELL 3 PROGRAM PROOF AND MANIPULATION 2 Some techniques for proving correctness of programs which alter data structures.

Lists provide the most obvious and natural representation of literals because lists perfectly reflect function nesting structure. A list is also a reasonable representation of a set, in particular of a clause. Lists, however, can consume large amounts of space, and cause frequent garbage collections. We shall present in this paper a representation of clauses and literals which is as natural as lists but far more compact. We achieve this economy by sharing the structure of the parents of a resolvent in our representation of the resolvent. A clause is a set of literals; but throughout this paper we shall speak of the literals of a clause as having an order. That is, we shall speak of the first, second, etc., literal of a clause.

C. COOPER 21 3 Data representation--the key to conceptualisation: D. B. VIGOR 33 MECHANISED MATHEMATICS 45 4 An approach to analytic integration using ordered algebraic expressions: L. I. HODGSON 47 5 Some theorem-proving strategies based on the resolution principle: J. L DARLINGTON 57 MACHINE LEARNING AND HEURISTIC PROGRAMMING 73 6 Automatic description and recognition of board patterns in Go-Moku: A. M. MURRAY and E. W. Etcomc

Lisp, etc.) are designed for off-line use. Poi,-1 is for use by a person communicating directly with a computer via a typewriter. With this in mind I have aimed at a tolerable efficiency of execution, and an ability to define and name new operations, with comprehensive monitoring facilities. On the other hand actual error messages are rather simple. At the very lowest level, the computer can be used just as a desk calculator.

The equivalence problem for program schemes, or for programs, is reduced to the proving of a theorem in second-order logic. This work extends Manna's first-order logic reductions. Some examples of the technique are given together with a suggested method for obtaining proofs in special cases by firstorder methods. INTRODUCTION Several workers in recent years have considered using techniques and ideas of various mathematical theories of computation for proving interesting results about computer programs. This paper is concerned with two of these approaches.

R.M.BURSTALL 373 23 Assertions: programs written without specifying unnecessary order.

Networks as models of word storage: G. R. Kiss