The Accurate Reservoir Characterization Of Non-marine Sand-gravel Reservoir

Sand-gravel formation is typical of massive thickness, complicated lithology, sharp variation of facies; conventional water flooding sweep efficiency is poor in the water injection development due to the poor efficiency of injection-production system in the present well pattern and spacing. The interlayer interference is strong during commingled producing and percentage of recovered OOIP is low. Commingled production yielded uneven development among layers, thus bringing difficulty for subsequent development plan and studies. Detailed study and categorization of development unit of reservoir and accurate reservoir characterization is the key to solve the above mentioned problem.This paper has adopted the following key technologies and methodology based on comprehensive geological study:(1) Precise subdivision of formation structure is carried out through natural gamma ray spectrum combined with core observation and seismic data. (2) The stratigraphic correlation technology combined with seismic and logging data. (3) The sequence is precisely sorting out by high-resolution image logging to sand-gravel reservoir in conjunction with core observation through the adoption of high-resolution sequence stratigraphy. (4) The reservoir of non-marine sand-gravel is precisely evaluated by logging data. (5)3D geology model.The logging responses characteristics of parameters of spectral gamma ray as well as conventional logging have been fully studied with the innovative methodology of establishing stratigraphic framework incorporating seismic-sonic synthetic record. The strata of all wells is newly classified through correlating seismic profile and corresponding cross-well profile under the restriction of formation sedimentary modeling. High resolution image logging is adopted to identify sand-gravel reservoir and achieves the high resolution stratigraphy analysis of sand-gravel reservoir. Sedimentary microfacies identification by conventional logging is calibrated through abundant cores and image logging data. The artificial neural network is incorporated as well to define the type of continuous microfacies of single well and predict the distribution of interwell sedimentary microfacies by conventional logging data.The main pore type is characterized by secondary pore. The development and distribution of the pore is mainly subject to the evolution and corrosion of all sorts of authigenic minerals in the diagenesis. The log interpretation chart of porosity, permeability, saturation and shale content of gravel-sand is built in this paper. 3D geology model is built based on the conceptual model.