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The paper proposed method bridge a gap between geophysics data and geological knowledge.The paper proposed method solves 3-D poststack seismic inversion problem, is more useful to application of production and development.The paper proposed method generate models with low uncertainty and faster speed.

Autogenic processes are widely found in various sedimentary systems and they play an important role in the depositional evolution and corresponding sedimentary architecture. However, autogenic processes are often affected by changing allogenic factors and are difficult to be identified and analyzed from modern and ancient records. Through the flume tank experiment under constant boundary conditions, the depositional process, evolution principles, and the sedimentary architecture of a river-dominated delta was presented, and a corresponding sedimentary architecture model was constructed. The evolution of river-dominated delta controlled only by autogenic process is obviously periodic, and each autogenic cycle can be divided into an initial progradational stage, a middle retrogratational stage, and a late aggradational–progradational stage. In the initial progradational stage, one feeder channel incised into the delta plain, mouth bar(s) was formed in front of the channel mouth, and small-scale crevasse splays were formed on the delta plain. In the middle retrogradational stage, the feeder channel was blocked by the mouth bar(s) which grew out of water at the end of the initial stage, and a set of large-scale distributary splay complexes were formed on the delta plain. These distributary splay complexes were retrogradationally overlapped due to the continuous migration of the bifurcation point of the feeder channel. In the late aggradational–progradational stage, the feeder channel branched into several radial distributary channels, overlapped distributary channels were formed on the delta plain, and terminal lobe complexes were formed at the end of distributary channels. The three sedimentary layers formed in the three stages constituted an autogenic succession. The experimental delta consisted of six autogenic depositional successions. Dynamic allocation of accommodation space and the following adaptive sediments filling were the two main driving factors of the autogenic evolution of deltas.

Traditional stochastic reservoir modeling, including object-based and pixel-based methods, cannot solve the problem of reproducing continuous and curvilinear reservoir objects. The paper first dives into the various stochastic modeling methods and extracts their merits, then proposes the skeleton-based multiple point geostatistics (SMPS) for the fluvial reservoir. The core idea is using the skeletons of reservoir objects to restrict the selection of data patterns. The skeleton-based multiple point geostatistics consists of two steps. First, predicting the channel skeleton (namely, channel centerline) by using the method in object-based modeling. The paper proposes a new method of search window to predict the skeleton. Then forecasting the distributions of reservoir objects using multiple point geostatistics with the restriction of channel skeleton. By the restriction of channel centerline, the selection of data events will be more reasonable and the realization will be achieved more really. The checks by the conceptual model and the real reservoir show that SMPS is much better than Sisim (sequential indicator simulation), Snesim (Single Normal Equation Simulation) and Simpat (simulation with patterns) in building the fluvial reservoir model. This new method will contribute to both the theoretical research of stochastic modeling and the oilfield developments of constructing highly precise reservoir geological models. reservoir skeleton, multiple point geostatistics, restriction, Simpat (simulation with patterns), fluvial reservoir, geological modelThe fluvial reservoir is one of the most important hydrocarbon reservoirs and contains about 42.6% of total oil reserves in China [1] . So study on high resolution model of fluvial deposits is very important and practical.Reservoir modeling of fluvial objects has been deeply investigated and some stochastic modeling algorithms have been developed abroad, such as marked point process [2][3][4][5][6][7] , sequential indicator simulation (Sisim), Fluvsim [8] . However, the object-based methods (such as marked point process) face the problems of condition and parameterization, and the pixel-based methods cannot reproduce the curvilinear objects, reproducing continuous and curvilinear fluvial reservoir objects is a nutshell to reservoir modeling engineers.As we know, the pixel-based methods only reproduce the two-point statistics revealed by the variograms, which is inefficient for shape reproducing. Some reservoir engineers consider using multiple points to reproduce reservoir shapes [9][10][11] . Due to the large computation and repeatedly scanning the training image, these early iterative methods have not been used in practice. In 2000, Strebelle introduced a "search tree" to solve the repeated scanning problem and devised the modeling method of Snesim (Single Normal Equation Simulation) [12][13][14] . The computation time is reduced sharply by using the storage function of search tree. The multiple point geostatistics then has been accepted and put into use in real reserv...

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