四川盆地东北部某储卤构造深层卤水资源量容积法评价的改进模型研究

doi:10.11867/j.issn.1001-8166.2013.06.0703

地球科学进展 ›› 2013, Vol. 28 ›› Issue (6): 703 -708. doi: 10.11867/j.issn.1001-8166.2013.06.0703

周游 ^1，2，倪师军 ²，施泽明 ²

1.成都理工大学数学地质四川省重点实验室，四川成都 610059；2.成都理工大学地球化学系，四川成都 610059

收稿日期:2013-01-22 修回日期:2013-03-27 出版日期:2013-06-10
基金资助:
中国地质调查局项目“四川三叠纪富钾卤水富集规律及有利地区调查评价”（编号：1212011085518 ）；国家自然科学基金项目“原生晕地球化学三维空间分布研究及定量成矿预测”（编号：41272363）资助.

A Study of Improved Deep Brine Resources Evaluation Model of Volumetric Method of One Brine Structure in Northeastern Sichuan Basin

Zhou You ^1,2， Ni Shijun ²， Shi Zeming ²

1.Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059,China；
2.Department of Geochemistry, Chengdu University of Technology, Chengdu 610059, China

Received:2013-01-22 Revised:2013-03-27 Online:2013-06-10 Published:2013-06-10

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深层地下卤水资源量的评价具有较大难度，一方面由于深部地质及水文地质参数难以准确获取，另一方面地下卤水资源量评价方法尚无规范可循。传统的深层卤水区域资源量评价方法一般采用容积法，但容积法对储卤层空间形态描述较简单，同时也无法处理深层卤水各参数严重不均一的问题，容易引起误差的累积。提出了容积法的一种改进模型，首先利用Opengl API三维渲染技术来建立储卤层三维模型，再利用克里格法对储卤层三维模型进行网格化，建立模拟单元，用模拟的方法来解决各参数的不均一性，进而求取资源量。该方法继承了资源量评价中“圈区算量”的基本思想，可以获得更准确的计算结果。为了便于该方法的应用，进行了软件实现，并以四川盆地东北部某储卤构造为例对该构造资源量进行了试算。结果表明该方法能更充分地利用深部地质参数，是对容积法的一种有效改进。

关键词: 深层卤水, 三维模型, 克里格法, 资源量评价

Deep brine resource evaluation is a very difficult issue. On the one hand, deep brine’s geological and hydrogeological parameters are difficult to obtain, and on the other hand deep brine’s evaluation method is lack of standard. Traditional evaluation method of deep brine resources is volumetric method. But volumetric method is too simple to describe spatial form of brine reservoir, and it also can not solve the problem of deep brine parameters serious heterogeneity. Volumetric method is easy to cumulative error. This paper proposes an improved model of the volumetric method. At first, it uses Opengl API 3D rendering technology to create threedimensional model of brine reservoir. Then, it uses Kriging method to set grid on the brine reservoir 3D model and builds simulation unit on the brine reservoir 3D model. At last, it uses simulation methods to solve the problem of parameters serious heterogeneity and calculates the resources. This method inherits the traditional calculating ideas, and it can obtain more accurate calculation result. In order to apply this method, a software is develops. This paper evaluates one brine structure’s resources of Northeastern Sichuan Basin as an example. This method can more fully exploit the deep geological parameters, and its result is better than volumetric method. This method is an effective improvement of the volumetric method.

Key words: Deep brine, 3D model, Kriging method, Resource evaluation

中图分类号:

P641.8

[1]Li Cijun, Yang Lizhong, Zhou Xun, et al. Resource Quantity Evaluation of Deep Brine[M]. Beijing:Geological Publishing House, 1992.[李慈君，杨立中，周训,等.深层卤水资源量评价的研究[M].北京：地质出版社，1992.]

[2]Zhou Xun. An attempt to evaluate resources of deep-seated subsurface brines using the finite element method[J].Geological Review,1990,36(4):376-380.[周训.试用有限单元法评价深层地下卤水资源量[J].地质论评，1990,36（4）：376-380.]

[3][JP2]Yang Lizhong.Time series analysis of the evaluation of the deep buried underground brine resource[J].Hydrogeology & Engineering Geology,1990,5:11-14.[杨立中.深层地下卤水资源量评价的时间序列分析研究[J].水文地质工程地质，1990,5：11-14.]

[4]Zheng Mianping, Yuan Heran, Zhang Yongsheng, et al. Regional distribution and prospects of potash in China[J]. Acta Geologica Sinica, 2010,84（11）：1 523-1 551.[郑绵平,袁鹤然,张永生,等.中国钾盐区域分布与找钾远景[J]. 地质学报，2010,84（11）：1 523-1 551.]

[5]Liu Li,Ye Wen. Precipitation prediction of time series model based on BP artificial neural network[J].Journal of Water Resources & Water Engineering, 2010,21（5）：156-159.[刘莉，叶文.基于 BP神经网络时间序列模型的降水量预测[J].水资源与水工程学报，2010,21（5）：156-159.]

[6]Ni Shijun，Teng Yanguo， Zhang Chengjiang， et al. Review on the geochemical tracing of mineralizing fluid[J]. Advances in Earth Science, 1999,14(4):346-352.[倪师军，滕彦国，张成江，等. 成矿流体活动的地球化学示踪研究综述[J].地球科学进展，1999,14（4）：346-352.]

[7]Zharkov M A. Paleozoic Salt-Bearing Formations of World[M]. Berlin:Spring-Verlag，1984.

[8]Wang Yunpu，Wang Huanfu， Wang Dongsheng, et al. Deep Brine Formation and Its Research Methods[M].Beijing:Geological Publishing House, 1982.[汪蕴璞，王焕夫，王东升,等.深层卤水形成问题及其研究方法[M].北京：地质出版社，1982.]

[9]Zharkov M A. History of Paleozoic Salt Accumulation[M].Berlin:Spring-Verlag,1981.

[10]Scotese C R,Bambach R K,Barton C,et al. Palaeozoic base maps [J]. Geology, 1979, 87 (3) :217-277.

[11]Zhou You, Ni Shijun, Shi Zeming. Research on time series neural network model of recoverable deep brine resources evaluation[J]. Scientific and Technological Management of Land and Resources,2012,29（3）：54-59.[周游,倪师军,施泽明.深层卤水可采资源量评价的时间序列神经网络模型研究[J].国土资源科技管理，2012,29（3）：54-59.]

[12]Zhang Chengjiang, Xu Zhengqi, Ni Shijun, et al. Genesis of potassium-bearing brine in Pingluoba structure region, western Sichuan Depression[J]. Advances in Earth Science, 2012,27(10):1 054-1 060.[张成江，徐争启，倪师军,等.川西拗陷平落坝构造富钾卤水成因探讨[J].地球科学进展，2012,27（10）：1 054-1 060.]

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