Driving mechanism for farmers' participation in improving farmland ecosystem: Evidence from China

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A Novel Method of 3D Multipoint Geostatistical Inversion Using 2D Training Images

AbstractThe seismic inversion method combined with multipoint geostatistics theory has begun to receive attention, but the acquisition accuracy and calculation efficiency of 3D training image still need more optimization. This paper presents a novel method of 3D multipoint geostatistical inversion based on 2D training images directly. The 2D training image was scanned by the data template to acquire the multipoint statistical probability in 2D direction. The probability fusion method is used to fuse the 2D multipoint probability into 3D multipoint probability. The rock facies types and patterns of the simulated points are obtained by random sampling. On this basis, the elastic parameters are extracted from the statistical rock physics model, and the seismic records are convoluted. Then, the synthetic records and the actual records were compared under a given threshold. If the error exceeds the given threshold, the iterative adaptive spatial sampling method will be used to repeat the process above-mentioned, so as to ensure that the error is below the threshold. Because the 2D training image is easy to obtain and evaluate, the demand problem of 3D training image is solved. The 2D training image scanning, probability storage and access are more convenient, and the adaptive spatial sampling method is more efficient than the reject sampling, so as to ensure the operation efficiency. The model from the Stanford Center for Reservoir Forecasting is selected to test the effectiveness of this newly designed method

Disrupted Balance of Long- and Short-Range Functional Connectivity Density in Type 2 Diabetes Mellitus: A Resting-State fMRI Study

Previous studies have shown that type 2 diabetes mellitus (T2DM) can accelerate the rate of cognitive decline in patients. As an organ with high energy consumption, the brain network balances between lower energy consumption and higher information transmission efficiency. However, T2DM may modify the proportion of short- and long-range connections to adapt to the inadequate energy supply and to respond to various cognitive tasks under the energy pressure caused by homeostasis alterations in brain glucose metabolism. On the basis of the above theories, this study determined the abnormal functional connections of the brain in 32 T2DM patients compared with 32 healthy control (HC) subjects using long- and short-range functional connectivity density (FCD) analyses with resting-state fMRI data. The cognitive function level in these patients was also evaluated by neuropsychological tests. Moreover, the characteristics of abnormal FCD and their relationships with cognitive impairment were investigated in T2DM patients. Compared with the HC group, T2DM patients exhibited decreased long-range FCD in the left calcarine and left lingual gyrus and increased short-range FCD in the right angular gyrus and medial part of the left superior frontal gyrus (p < 0.05, Gaussian random-field theory corrected). In T2DM patients, the FCD z scores of the medial part of the left superior frontal gyrus were negatively correlated with the time cost in part B of the Trail Making Test (ρ = -0.422, p = 0.018). In addition, the FCD z scores of the right angular gyrus were negatively correlated with the long-term delayed recall scores of the Auditory Verbal Learning Test (ρ = -0.356, p = 0.049) and the forward scores of the Digital Span Test (ρ = -0.373, p = 0.039). T2DM patients exhibited aberrant long-range and short-range FCD patterns, which may suggest brain network reorganization at the expense of losing the integration of long-range FCD to adapt to the deficiency in energy supply. These changes may be associated with cognitive decline in T2DM patients

A location-based multiple point statistics method: modelling the reservoir with non-stationary characteristics

In this paper, a location-based multiple point statistics method is developed to model a non-stationary reservoir. The proposed method characterizes the relationship between the sedimentary pattern and the deposit location using the relative central position distance function, which alleviates the requirement that the training image and the simulated grids have the same dimension. The weights in every direction of the distance function can be changed to characterize the reservoir heterogeneity in various directions. The local integral replacements of data events, structured random path, distance tolerance and multi-grid strategy are applied to reproduce the sedimentary patterns and obtain a more realistic result. This method is compared with the traditional Snesim method using a synthesized 3-D training image of Poyang Lake and a reservoir model of Shengli Oilfield in China. The results indicate that the new method can reproduce the non-stationary characteristics better than the traditional method and is more suitable for simulation of delta-front deposits. These results show that the new method is a powerful tool for modelling a reservoir with non-stationary characteristics

Reconstruction of 3D Reservoir Lithological Model Using 2D Facies Profiles in SU 36-11 Area of Ordos Basin, China

In the middle and late stages of gas field development, the establishment of a fine reservoir lithological model is an important basis for drilling well pattern adjustment and potential exploitation. The SU 36-11 area of the Ordos basin in China is developing braided channel sediment with rich gas resources. However, the success rate of drilling wells is low due to the complex reservoir heterogeneity and the lack of a fine reservoir lithological model. In this paper, the complex internal structure of the reservoir sand body is revealed using the architectural element analysis method. Three sand body models, that is, isolated channel, superimposed channel, and cut superimposed channel, can be recognized. The effective sand body is mainly the channel bar deposit with a thickness of 2–5 m, a width of 200–500 m, a length of 400–700 m, a width ratio of 50–120, and a length-to-width ratio of 1.5–2. The 2D maps of the lithofacies (architectural elements) were then digitized to create 2D training images (TI) for the construction of the 3D model. The 2D data template was selected to scan the TI to obtain the 2D multi-point probability. The 3D multi-point probability was then generated using the probability fusion theory. The Monte Carlo sampling was used to predict the lithological type between wells. Finally, the 3D reservoir lithological model was built directly using the 2D lithological profiles. From the model, the geometry of the braided channel, channel bar, and flood plain was well revealed, and the spatial distribution of effective reservoir sand bodies was accurately predicted. The cross-validation test shows that the error of the channel bar is 6.5% on average, which improves the accuracy of the prediction of lithology in the sub-surface and can be used to guide the subsequent development of residual gas

Sedimentary architecture of thin-layer beach bar sand bodies in the G oilfield, Niger

The G oilfield in the southeastern Termit Basin of Niger is characterized by thin-bed beach bar deposits exhibiting strong reservoir heterogeneity and suboptimal production efficiency, necessitating internal structural dissection of the beach bar sand bodies. Employing a well-seismic integration approach, we systematically dissect the architecture of these sand bodies layer by layer to determine their spatial distribution. A classification scheme for beach bar architecture is proposed, with core and log data analysis identifying essential architectural elements and their logging responses. Seismic amplitudes, thin bed delineation, frequency decomposition inversion attributes, and attribute fusion technology delineate the architectural boundaries. Integrating five indicators from four-level architectural recognition at wellbores—shallow lake mudstone appearance, bar margin/beach microfacies occurrence, logging curve morphology differences, beach bar thickness variations, and elevation differences between adjacent bars—enables detailed dissection of the beach bar architecture, corroborated by connectivity analysis. In the study area, beach bar distribution primarily develops in two modes: vertical stacking (accumulation of multiple main bars from different episodes) and isolated (stable mudstone interlayers between main bar sand bodies appearing relatively isolated). This research provides a basis for dissecting beach bar architecture reservoirs under sparse well conditions

A Multi-Point Geostatistical Seismic Inversion Method Based on Local Probability Updating of Lithofacies

In order to solve the problem that elastic parameter constraints are not taken into account in local lithofacies updating in multi-point geostatistical inversion, a new multi-point geostatistical inversion method with local facies updating under seismic elastic constraints is proposed. The main improvement of the method is that the probability of multi-point facies modeling is combined with the facies probability reflected by the optimal elastic parameters retained from the previous inversion to predict and update the current lithofacies model. Constrained by the current lithofacies model, the elastic parameters were obtained via direct sampling based on the statistical relationship between the lithofacies and the elastic parameters. Forward simulation records were generated via convolution and were compared with the actual seismic records to obtain the optimal lithofacies and elastic parameters. The inversion method adopts the internal and external double cycle iteration mechanism, and the internal cycle updates and inverts the local lithofacies. The outer cycle determines whether the correlation between the entire seismic record and the actual seismic record meets the given conditions, and the cycle iterates until the given conditions are met in order to achieve seismic inversion prediction. The theoretical model of the Stanford Center for Reservoir Forecasting and the practical model of the Xinchang gas field in western China were used to test the new method. The results show that the correlation between the synthetic seismic records and the actual seismic records is the best, and the lithofacies matching degree of the inversion is the highest. The results of the conventional multi-point geostatistical inversion are the next best, and the results of the two-point geostatistical inversion are the worst. The results show that the reservoir parameters obtained using the local probability updating of lithofacies method are closer to the actual reservoir parameters. This method is worth popularizing in practical exploration and development

Preliminary study on a depositional interface-based reservoir modeling method

According to the channel deposition process and response features, the reservoir modeling based on the depositional interface (DI) is extended from turbidite fans to fluvial sandstones, in order to reconstruct the deposition process and improve the architecture simulation of channel sands. The DI-based modeling of fluvial reservoir is conducted in four steps: (1) Use a simple harmonic oscillation (SHO) damping model with disturbance to generate river flow lines and then complete the beaded association of singe-genetic sands; (2) Identify the interfaces at ends of channels (point bars) to generate the point bar model; (3) Determine the stacking pattern inside channel (point bar), and fit the interfaces with such functions as hyperboloid, paraboloid and polynomial; and (4) Carry out random sampling using the trigonometric function of key parameters for lateral accretion bedding, to complete characterization of point bar. It is concluded that the DI-based modeling method well reproduces the depositional process of fluvial sandstones and finely characterizes the architecture units therein. Key words: DI-based modeling, depositional process, reservoir architecture unit, channel sand, reservoir modeling metho

Distribution of remaining oil based on a single sand body analysis: a case study of Xingbei Oilfield

Abstract The description of a single sand body for remaining oil predictions is critical to the enhancement of oil recovery of an old oilfield. Taking the fluvial facies of the Xingbei Oilfield as an example, a single sand body can be divided into four categories—“tabulated reservoir”, “untabulated reservoir”, “single channel sand body” and “abandoned channel”—using the reservoir architecture analysis method. The boundary surface of each type may be mud barriers or only an erosion surface, which was traced by careful anatomization of the single sand body. Then, a fine single sand body reservoir geological model was constructed using a combination of a determined modelling method and stochastic modelling method. The numerical simulation is executed using the constructed geological model to forecast the remaining oil distribution quantitatively. The results show that the remaining oil was distributed in the bottom parts of the abandoned channel, top part of the point bar, tabulated reservoir, and channel edges. The movements of the injection water were mainly controlled by the mud barrier and superimposed styles of single sand body, which determines the formation of the remaining oil. This research has important guidance for oilfield development in the late stage, whose reservoir is composed of single sand bodies