A Fractal Simulation Method for Simulating the Resource Abundance of Oil and Gas and Its Application

In this study, a fractal simulation method for simulating resource abundance is proposed based on the evaluation results of the exploration risk and prediction technology for the spatial distribution of oil and gas resources at home and abroad. In addition, a key technical workflow for simulating resource abundance was developed. Furthermore, the model for predicting resource abundance has been modified, and the objective functions for conditional simulation have been improved. A series of prediction technologies for predicting the spatial distribution of oil and gas resources have been developed, and the difficulties in visualizing the exploration risks and predicting the spatial distribution of oil and gas resources have been solved. Prediction technologies have been applied to the Jurassic Sangonghe Formation in the hinterland of the Junggar Basin, and good results have been obtained. The results indicated that within the known area, taking the known abundance as the constraint condition, the coincidence rate of the simulated quantities of the original model and the improved model with the actual reserves reached 99.98% after the conditional simulation, indicating that the conditional simulation was effective. In addition, with the improved model, the predicted remaining resources are 0.7899$$\times 10^{8}$$ × 10 8 t, which is 65% of the discovered reserves, and the original model predicts that the remaining resources are 3.5033$$\,\times \,10^{8}$$ × 10 8 t, which is 2.89 times greater than the discovered reserves. Compared with the area in the middle stage of exploration, the results of the improved model are more consistent, and the results of the original model are obviously larger, indicating that the improved model has a good predictive effect for the unknown area. Finally, according to the risk probability and remaining resource distribution, the favorable areas for exploration were optimized as follows: the neighborhood of the triangular area formed by Well Lunan1, Well Shimo1, and Well Shi008, the area near Well Mo11, the area east of Well Mo5, the area west of Well Pen7, the area southwest of Well Shidong1, and the surroundings, as well as the area north of Well Fang2. The application results show that these prediction technologies are effective and can provide important reference and decision-making for resource evaluation and target optimization.

A Conditional Simulation Method for Predicting Wind Pressure Fields of Large-Span Spatial Structures

Wind load is among the control loads for large-span spatial structures. Wind tunnel test is one of the commonly used methods for measuring wind pressure fields of different kinds of structures. However, due to the limited wind pressure data obtained from wind tunnel testing, it is quite meaningful to employ the limited measured data to predict the unknown wind pressure at target points. Considering the complexity of wind pressure fields of large-span spatial structures, a simplified nonparametric method based on conditional simulation is proposed to predict the unknown pressures using the existing data. The Karhunen–Loève (KL for short) expansion is employed to represent wind pressure random variants as eigenfunctions of the covariance operator. To reduce the variant dimensionality, the nearest neighboring estimator is given for the transition distribution of the KL expansion. The targeted wind pressure fields are obtained by expanding the Fourier basis of the eigenfunction and estimating its expansion coefficients. The proposed method is applied to estimate wind pressures on a gable roof building. The relevant parameters of the wind pressure field are obtained, and the results compare well with those from wind tunnel testing, with higher efficiency. The proposed method effectively reduces the dimensionality of the predicted wind pressures, with reduced errors, higher accuracy, and increased efficiency.

Multiple-point geostatistical reconstruction of GPR reflection data

<p>A common challenge in reflection GPR data processing and analysis is the reconstruction of missing traces. Gap filling, for example, may be needed to fill-in data where they could not be recorded in the field in order to produce a uniform trace spacing that is important for Fourier- or finite-difference-based migration methods. Similarly, field GPR data recorded in continuous mode with an uneven trace spacing are usually needed at a regular spacing for subsequent visualization and imaging. Finally, we may wish to increase the spatial resolution of a GPR dataset through “super-resolution”, whereby new traces are simulated between the existing ones in order to improve the interpretability of the data. A common challenge in these various applications is the need to interpolate a variable that has a complex, non-smooth behavior.</p><p>A number of interpolation methods have been proposed for filling in missing GPR traces over the past decades. The majority of these, however, tend to produce overly smooth and unrealistic results. Here, we present a data reconstruction strategy based on the QuickSampling (QS) multiple-point geostatistical method. With this approach, GPR traces are simulated via sequential conditional simulation based on patterns that are observed in nearby high-resolution data (training images). To evaluate the potential of our approach, we apply it to a variety of field 2D GPR datasets. Results indicate that the QS method provides an effective means of simulating missing GPR traces in a highly realistic manner.</p>

Simulation of rainfall fields conditioned on rain gauge observations and radar estimates using random mixing

The accuracy of spatial precipitation estimates with the relatively high temporospatial resolution is of vital importance in various fields of research and practice. Yet the intricate variability and the intermittent nature of precipitation make it very difficult to obtain accurate spatial precipitation estimates. Radar and rain gauge are two complementary sources of precipitation information: the former is inaccurate in general but is a valid indicator for the spatial pattern of the rainfall field; the latter is relatively accurate but lack spatial coverage. Considering the pros and cons of the two sources of precipitation information, a number of radar-gauge merging techniques have been developed to obtain spatial precipitation estimates over the past years. Conditional simulation has great potential to be used in spatial precipitation estimation. Unlike the commonly used interpolation methods, the results from the conditional simulation is a range of possible estimates due to its Monte Carlo framework. Yet an obstacle that hampers the application of conditional simulation in spatial precipitation estimation is how to obtain the marginal distribution function of the rainfall field with accuracy. In this work, we propose a method to obtain the marginal distribution function of the rainfall field based on rain gauge observations and radar estimates. The conditional simulation method, random mixing (RM), is used to simulate rainfall fields. The properties of the results from the proposed method are elaborated through the comparison with the results from other methods: ordinary kriging, kriging with external drift, and conditional merging. Finally, the sensitivity of the proposed method towards the two factors – density of rain gauges and random error in radar estimates – is analyzed.