The Evaluation And Prediction Of Oil Sands Reservoir In Western Slope, Songliao Basin

At present, with the increasing of global energy requirements, while conventional oiland gas resources has begun to dramatically reduce, a huge gap energy future largely onthe need to rely on unconventional oil and gas to make up. The Seventh InternationalConference on tar sands and heavy oil was held in1998. Countries are increasinglyrecognizing the importance of unconventional energy. Oil sands are very importantunconventional energy, increase research on oil sands deposits, exploration anddevelopment efforts.In recent years, oil sands deposits have been discovered in China’s major oil and gasbasins by geologists. Study area is located in the overlap zone which belongs to thewestern slope of Songliao basin which is one of the oil sands deposits of the mostpotential for development. Previous studies suggest that the kinds of reservoirs arecomplex, including structural reservoir, lithological reservoir and structural-lithologicalreservoirs.Currently, the domestic means of oil sands deposits exploration and developmentmainly by core drilling. Then, test and analysis the core samples. Finally, oil sandsthickness, oil content and physical property values can be got, such as porosity,permeability, saturation. The disadvantage of these research methods is prolongedexploration development and increase economic costs. Thus the significance of this studyis to: find an effective method for a suitable oil sands reservoir evaluation and prediction,thereby improving economic efficiency and reduce exploration and development costs.Basic data used in this study including drilling, well logging, core and geochemistry information. The experimental methods used include microscopic observation anddescription of rock slice and cast thin section, X diffraction analysis of whole rock andclay mineral composition analysis, scanning electron microscopy and geochemicalexperiments testing.The research methods include identification of lithology, mineral composition andpore type, logging interpretation and evaluation, lithofacies and sedimentary faciesanalysis, and neural network. The research include: ascertain lithology and pore type;establish well logging evaluation model; fine layer correlation at the base of sedimentaryfacies spreading regularity, master oil sands reservoir distribution characteristics andconnectivity; predict the oil sands using BP neural network method.The innovations of this study are mainly two things:1. Establish the oil sands reservoir logging evaluation model: improved theconventional oil and gas reservoir logging evaluation method, established for thecalculation of oil sands reservoir physical properties formula;2. Forecasting oil sands use of BP neural network, to obtain more satisfactory results.It is the first time in the domestic to use neural networks method to predict the oil sandsreservoir. The method must be constantly updated and improved.Through this study, the following conclusions recognize:1. Determine the lithology, clay mineral type and pore types of reservoir.(1) Through the core description, microscopic thin section analysis and other studiesto determine the lithology of study area are mainly clastic, most of which are feldsparlithic sandstone and lithic feldspathic sandstone, found only a small number of feldsparquartz sandstone and feldspathic sandstone. The lithology can be divided into mudstone,siltstone, fine sandstone, sandstone and a small amount of fine conglomerate according toparticle size.(2) The clay mineral content of this shallow oil sands reservoir in study area isrelatively low, clay minerals belong illite smectite mixed layer, which containingmontmorillonite, illite and kaolinite with minor amounts.(3) The pore types of oil sands reservoir include primary intergranular porosity,secondary dissolution pores and overgrowth edge solution pores, and with mainly primary intergranular porosity.(4) Reckoning the plane porosity by laboratory, the plane porosity is about15%, themaximum aperture is0.1to0.2mm, pore—throat coordination number is2to3, theconnectivity is well.2. Establish oil sands logging evaluation mode of research area.Fitting calculation formula of porosity. Using the known data, fitting porosity andinterval transit time, density and neutron porosity corresponding formula. The accuracyrate is more than85%. The actual prediction porosity, use of three formulas weightedaverage, so more accurate.In this study, oil sands reservoir saturation calculated using Archie formula, when theparameter b=1, m=n=2, we found that when the depth between180and200m, a isoften too small values of between0.4to0.6, calculated saturation of high precision, closeto the known value; when the depth is about150m, a value is close to1, calculatedsaturation of high precision. Experience the value of a general case from0.6to1.5, andthe low value of this study. It is the special nature of the shallow oil sands in research area.Determining permeability can choose a theoretical calculation formula can be.3. Establish a BP neural network to forecast oil sands. Forecast oil sands using BPneural network, the effect is significant. In the future it can be widely promoted andapplied this method in the oil sands reservoir evaluation and prediction.4. Predict the distribution of oil sands research area:(1) The oil sands are mainly in margin area west slope of Songliao basin;(2) The main reservoir unit of oil sands corresponds with sedimentary microfaciestypes are underwater distributary channel, mouth bar, distal bar and other advantagessedimentary microfacies. The better physical properties of sand bodies constitute a majoroil sands reservoir space;(3) The forecasting distribution of oil sands using neural network method is basicallythe same as sedimentary facies distribution. The oil sands development areas are middle,southern and northern in the study area.