Reservoir Properties Distribution Forecast in a Complex Carbonate Reservoir and Drilling Risk Evaluation

Abstract

Abstract At the present time the geological and hydrodynamic simulation is a standard tool for the analysis and design of oil and gas field development. Usually the full-field models are used for reserves estimation and preparation of Project design documentation. In addition, lots of enterprises use geological and simulation models (G&SM) for operational purposes such as adjustment of current well scheme, planning of wellworks etc. Using the G&SM for these purposes requires their constant operational updating on the newly received geological and geophysical information. These models are called as continuously updated geological and simulation models (CUG&SM). In order to construct such models and improve the predictive capability we apply approaches that are different from approaches that are used in preparation of project documents for undergoing the state examination. This paper reviews alternative approaches to improve the quality of G&SM in terms of oilfields of LLC "JC "RUSVIETPETRO", located in the Central Khoreyver Uplift in the Khoreyver depression area in the northern part of the Timan-Techora Oil and Gas province. Due to the high heterogeneity of carbonate reservoirs, high anisotropy properties and high degree of influence on the petrophysical rock properties (RP) of secondary processes, the applying of standard methodologies of oilfields simulation does not bring any expected results and it may introduce significant errors in estimating of oil and gas reserves. The development of alternative simulation approach is necessary aspect for construction of geological models (GM) which are able to adequately estimate the current geological situation at the oilfields. The developed algorithm requires the adaptation to each new object of study depending on the availability of input data and the geological aspects, only a highly specialized tool can provide high predictive capability under the conditions of fractured porous cavernous carbonate reservoir. For the distribution of RP in the interwell space, a special simulation algorithm with the neural networks, based on the wells and seismic data, has been developed. For the construction of a curve Pc in wells, we apply an artificial neural network (ANN). ANN is trained by means of neuron and gamma logs and results of core studies. In this case, ANN is used to solve the problems of porosity prediction and optimization of operational process. To recover properties in interwell space at a minimum of priory information and to estimate earth parameters based only on seismic and well data is possible by applying genetic inversion. In this case ANN are not programmed in usual way, they are trained by means of known data – seismic roads in the well area and logging. During the process of training ANN is able to analyze, identify and summarize complex nonlinear functions and calculate values that are not in the training set. This algorithm relates to express method of elastic properties estimation. Based on the developed GM, the optimization of well location was carried out. The actual drilling results confirmed the correctness of the taken decisions. The G&SM was developed, the conformity of planned and actual fluid flow rates, water cut, Rp dynamics proved the high efficiency of the method.