3D Hydrogeological Structure Modeling Based on TPROGS A Case Study from the West Liaohe Plain

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1 nd International Conference on Material Engineering and Application (ICMEA 2015) ISBN: D Hydrogeological Structure Modeling Based on TPROGS A Case Study from the West Liaohe Plain Qian Sun, Jingli Shao, Yali Cui & Qiulan Zhang School of Water Resources and Environment, China University of Geosciences, Beijing, China ABSTRACT: According to the hydrogeological conditions of the West Liaohe Plain, we analyzed the effects of major parameters and selected the Markov chain model reasonably with transition probability-based geostatistics; with sequential indicator simulation in the conditional simulation and effective combination of geological knowledge with borehole data, we established a reasonable hydrogeological structure model. Besides, we carried out vacuating inspection and optimization on this model to select the optimized one which conforms to the actual geological conditions. The model can well display the distribution trend of various lithologies, and the simulation structure is consistent with the actual geological conditions. Finally, we carried out a brief analysis on the major parameters and uncertainty of the model. 1 INTRODUCTION 3D hydrogeological modeling is to identify hydrogeological structure, ascertain the variability of geological space and realize the 3D visualization, so as to more visually and intuitively describe the spatial distribution law of aquifer and provide basic data for estimating hydrogeological parameters and establishing groundwater flow model. TPROGS (Transition Probability Geostatistical Software) is an open source software package aiming at the application of Markov chain and developed based on the GSLIB geological open source library (Steven F. Carle, 1999). Compared to the traditional geo-statistical methods, it has many advantages. At present, this software has been embedded into GMS. But under the situations of large research area, finer vertical subdivision and very large calculation quantity, it is more appropriate to carry out the calculation through external codes. But the massive visualization of TPROGS cannot meet these requirements, and the 3D visualization therefore needs to be further researched. This paper puts forward new standards for the model inspection to better measure its advantages and disadvantages. And otherwise, it has an important significance to know the uncertainty of the 3D hydrogeological structure model. 35

2 2 OVERVIEW OF THE RESEARCH AREA The West Liaohe Plain, located in the eastern part of Inner Mongolia Autonomous Region, is geotectonically within the central Inner Mongolia geosynclinal fold system. Since the Quaternary period, this area has been mainly suffered from subsiding movement, with100~250m thick Quaternary unconsolidated clasts deposited on the basement of Neogene mudstone. The deposition generally features a thick center and thin margin, with deposition center in Kailu County. The overall lithologic variation is shown as follows: laterally, sedimentary grains become finer from the piedmont margin to the plain as well as from the upstream region to the downstream region, transiting from pebble gravel, sand gravel to coarse sand, medium sand, fine sand and silt, and clay partings become more with increasing thickness. Vertically, a rhythmic succession features coarse grains at the bottom and fine grains at the top. The total sedimentary area is approximately 40,000km2. The working area has a total of 577 borehole data. According to the bottom formation encountered by boreholes, almost all boreholes have penetrated the Quaternary strata. According to the water-physical property of lithology, borehole lithology is generally divided into clay, silt, fine sand, and medium-coarse sand & gravel which are respectively expressed as 1, 2, 3 and 4. A total of 558 boreholes from the research area are involved in the stimulation in this research, and 19 boreholes have been reserved for the model testing. 3 HYDROGEOLOGICAL STRUCTURE MODEL 3.1 Model establishment TPROGS mainly includes three modules: GAMEAS, MCMOD and TSIM. The subdivision element of the model is 1,000m 1,000m 5m. Azimuth is set as -30, and the step length can be set as 1m. Through calculation and debugging, the proportion and extension length of four lithologies are calculated, as well as their vertical and lateral transition probability matrix of the Markov chain. Then we plot the fitting curve of vertical Markov chain and measured transition probability (Fig.1). Fig.1 shows the proportion and extension length of fine sand, gravel and medium coarse sand are larger, which coincides with that the West Liaohe Plain is lithologically dominated by unconsolidated clasts, with minor clay partings occurring in the downstream region. The good fitting between measured transition probability and Markov chain implies the vertical Markov chain established can really reflect the vertical variation of various lithologies in the West Liaohe Plain. The appropriate lateral extension length needs to be set for the establishment of the lateral Markov chain according to Walther's law, which has a great influence on the hydrogeological structure. In the conditional simulation, the number of 36

3 implementations, annealing parameters and random seeds need to be set. Finally, multiple simulation results of the 3D hydrogeological model of the West Liaohe Plain are generated. Figure 1. Vertical transition probability matrix graph of four lithologies. 37

4 3.2 Model inspection and optimization The generated simulation implementation shall be verified so as to be better applied in practice. Vacuating inspection is a common method to inspect the reliability of the modeling results. Before modeling, some boreholes shall be vacuated for not participating in calculation; after modeling, model boreholes shall be compared with the vacuated boreholes, in order to inspect their similarities. The inspection algorithm is shown in Formula below; the larger the target value is, the more accurate the model is. Finally, in these 100 groups of implementations, all the target values inspected are more than 70% obj = n * n i= ( IDmeasured ( i) IDmod el ( i )) (1) In the formula above, ID is the lithology generalization value of measured borehole, measured I D m o d e l ( i ) is the lithology generalization value of model borehole, marks the logic operation OR, and n represents the number of data. The optimization of the model shall be carried out to select the implementation with maximum inspection target value and then make the vacuated boreholes participate in the calculation. Fig. 3 shows the finally-optimized 3D hydrogeological structure model of the West Liaohe Plain. In order to see clearly the internal structure of the model, its grid map was made, as shown in Fig. 4. Fig. 3 and 4 show grains in the piedmont at the west side of the West Liaohe Plain are relatively coarse; and gradually become finer towards east, and silt and clay partings become more in number and thicker, which is consistent with the actual situation 3.3 3D Display The result generated by the conditional simulation is the lithology code simulated by each rectangular solid cell. Its own visualization module has a larger limitation, which can only display massive structure. We use Tecplot, an engineering drawing visualization software,-to display three-dimensional maps, as well as apply certain skills in data processing. Firstly, the cells outside the border and the roof and floor were set as inactive cells, and their lithology was set as 0; this step of operation was programmed through the external documents of Excel and GMS. Secondly, the data format was processed into XYZM format, namely space coordinates and lithology. Finally, Tecplot was used to display the three-dimensional map, data import and blanking of the inactive cells were conducted; the final 3D map is shown in Fig. 2, and its grid map is shown in Fig. 3 38

5 Figure 2. 3D hydrogeological structure model of the West Liaohe Plain. Figure 3. Grid map for the 3D hydrogeological structure model of the West Liaohe Plain. Irregular profiles cannot be directly drawn in Tecplot; therefore, programming processing was adopted. Extract borehole positions from profiles, and connect them, forming a 2D piecewise linear function; extract points from profiles by a certain spacing, calculate the lithologies of all points extracted from profiles, and display them in Tecplot (Fig. 5). 4 DISCUSSION ON MAJOR PARAMETERS AND UNCERTAINTY ANALYSIS 4.1 Effects of lateral extension length (1) (2) (3) (4) Figure 4. Comparison for effects of lateral extension length on simulation results (vertically magnified by100 times. During the establishment of the lateral Markov chain, the set of lateral extension length is required to be considered, which poses great effects on the structure of the geological model. Situation (1)-(4) represent four different lateral extension lengths, that is vertically extension lengths of 10 times, 100 times, 1000 times, 2000 times. Fig. 4 shows the simulation results of the extension length corresponding to that. According to the simulation results, the smaller the extension length is, the poorer the model continuity is, and various lithologies are very dispersive (see Fig. (1) and Fig. (2)); with the increase of extension length, the model continuity is strengthened gradually (see Fig. (3) and Fig. (4)). Compared to boreholes, it can also be found that Fig.(3) shows fine sand and medium-coarse sand media have a massive texture in the central west, which is consistent with the lithology distribution of boreholes in this section. But if the extension length is overlarge, the region which should not be continuous will be made 39

6 continuous, resulting in model distortion. The established hydrogeological model not only can meet randomness but also has structuredness only after the lateral extension length is adjusted to the reasonable scope. According to existing profile data and relevant geological data, the lateral extension length is set to be 1000 times of the vertical extension length in this study 4.2 Uncertainty analysis The sequential indicator simulation used in this study is a stochastic simulation, and shows a certain uncertainty in the region outside the boreholes participating in calculation. The currently-adopted geological modeling method is constrained by its modeling principle, which will result in the uncertainty of model. The hydrogeological structure modeling based on borehole data has no other geological constraints; it calculates and forecasts the lithology of the area between boreholes through geostatistics method, which has multiple possibilities. This is the uncertainty in the geological modeling. The correct understanding on the uncertainty has important significance in improving the rationality of modeling results and overcoming the blindness of application of the modeling results. The conditional simulation can generate multiple implementations according to random seeds, and a small change of random seeds generated by random machine will result in the unpredictable difference on the appearance of final implementation. The simulation methods, characteristic parameters, etc. used between multiple implementations are completely consistent. These results are all in equal position, which are equiprobable implementations, but there are many differences between them. In order to evaluate the uncertainty of the model, it is only necessary to inspect the simulated multiple implementations and unnecessary to optimize them. We selected the profiles trending from southwest to northeast for analysis (fig.5). Fig.5 shows the lithology at borehole position is changeless, which verifies the advantages of conditional simulation, i.e., the boreholes participating in calculation are taken as conditional constraints, their data are changeless during simulation, and the simulation results are not contradictory with objective and practical geological conditions. From west to east, the lithology of Borehole 118 is mainly gravel and medium coarse sand (Lithology 4), the lithology of Borehole QKZ4 is mainly fine sand (Lithology 3), and the lithology of Borehole ZKZ5 is mainly clay (Lithology 1), which 40

7 conforms to the law that grains from the piedmont to the plain become finer, featuring a lithologic transition. However, the difference between different implementations is very distinct, especially the uncertainty is more distinct between Borehole Wn5 and Borehole 118 and between Boreholes KZK2 and QKZ4; this is because that borehole spacing is large, and the simulation results for the intermediate region cannot be controlled. Conversely, the uncertainty between Borehole ZKZ6 and Borehole ZKZ5 is relatively weak because that their spacing is small. If the model uncertainty is reduced, we can increase the number of boreholes, decrease the borehole spacing or add other geological constraints. Figure 5. Comparison between four different implementation profiles with seven boreholes. 5 EQUATIONS (1) The geological model established by TPROGS (a geostatistical software) based on Markov chain/transition probability can well reflect the spatial anisotropy and simulate the structuredness and randomness of complex space. (2) The adopted sequential indicator simulation meets the process of borehole location data conditioning, i.e., the lithology of the boreholes involving in calculation is completely consistent with that of the boreholes after simulation. Conduit cross section may vary significantly over short distances. (3) Vacuating inspection was conducted on the model in this study and the effects of lateral extension length and drilling density on the model were discussed so as to finally obtain a geological structure model conforming to the actual geological conditions. (4) Through uncertainty analysis on the profiles with unevenly-distributed boreholes, it is found that the influence of quantity and location of boreholes on the uncertainty degree of 41

8 the model is larger. Furthermore, other geological constraints, (such as seismic data and profile data) can be added so as to make the model more accurate. (5) The hydrogeological structure model can simulate the spatial distribution of aquifer, which is applied to the water flow model so as to establish a more truthful and reliable hydrogeological conceptual model. REFERENCES D Agnese FA, Faunt CC, Turner AK, et al. Hydrogeologic evaluation and numerical simulation of the Death Valley regional ground water flow system[r]. Nevada and California. U. S. Geological Survey Water Resources Investigations Report, Han Shemin. Application of GMS in Visibility of Hydrogeological Structure [J]. Technology Forum, 2013(9): 44. Li Changqing, Shao Jingli. Study on the 3D modeling for hydrogeological structure of sediment plain based on the technique of conditional simulation [J], Journal of Geotechnical Investigation & Surveying, 2009: P Li Jun, Yang Xiaojuan, Zhang Xiaolong, et al. Lithologic Stochastic Simulation Based on the Three-dimensional Markov Chain Model [J]. Acta Petrolei Sinica, 2012(05): Li Li, Wang Yonggang. Overview of Geostatistics Application [J]. Progress in Exploration Geophysics, 2006, 29 (3): P J. Florian Wellmann, Franklin G. Horowitz, Eva Schill, et al, Towards Incorporating Uncertainty of Structural Data in 3D Geological Inversion[J]. Tectonophysics, 2010, 490: Ju Jianying, Shen Dongxuan. Development and Application of Bentonite in Engineering [M]. Beijing: China Building Material Industry Press, 2003 Maji, R., Sudicky, E.A., Panday, S., et al. Transition probability/markov chain analyses of DNAPL source zones and plumes [J]. Ground Water, 2006, 44(6): Ma Lei. Stochastic Modeling Method and the Application of Stochastic Modeling in Geological Model The Example of the Dagang Oilfield Kou Well Section [D], Chengdu University of Technology, 2011: P Steven F. Carle, TPROGS: Transition Probability Geostatistical Software[R], 1999, Weissmann, G.S., Carle, S.F., and Fogg, G.E. 3D hydrofacies modeling based on soil surveys and transi-tion probability geostatistics [J]. Water Resources Research, 1999, 35(6): Zhao Jiuwen, Duqing. Technique Synthetic Study about Massive Sense of Reality of Three-dimensional Terrain [J]. Geology of Chemical Minerals, 2006, 28(4):