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Collaborating Authors
Zuzhi, Hu
Mapping the Deep Upper Paleozoic based on joint of 3D MT-Gravity: A Case Study
Yanling, Shi (China University of Geosciences - Beijing and BGP - CNPC) | Zuzhi, Hu (BGP - CNPC) | Dechun, Li (BGP - CNPC) | Qiang, Wei (BGP - CNPC) | Sheng, Zhang (BGP - CNPC) | Li, Zheng (BGP - CNPC) | Liansheng, Ji (BGP - CNPC) | Cuixian, Men (BGP - CNPC)
Summary The existence of deep targets is of significant importance in basin exploration, especially in areas where few seismic datasets exist, and where it is a challenge to use magnetotelluric (MT) and gravity exploration to delineate the distribution of potential deep targets. Taking basin W as an example, this paper demonstrates that joint 3D MT and gravity exploration is effective for determining a basin’s deep hydrocarbon potential in a rapid and economic manner. This method is particularly useful for basins which have few seismic data, but are of interest for oil and gas exploration. Based on highprecision 3D MT data acquisition, which uses a 3D small-bin grid, datum static correction, and a 3D MT inversion method, the data accuracy has improved markedly, and the 3D resistivity data volume can be realized. Secondly, we can develop a layered geological color model with the electro-logging data, and set up a layered geological model. Finally, we adopt the interactive 3D MT and gravity inversion to further modify the previous geologic interpretation, and to delineate the distribution of potential deep targets. Introduction Basin W is a small-to-medium-size basin located to the south of the Erdos basin. It is not of immediate exploration concern, as it does not show great oil or gas zones. The Cenozoic strata are widely distributed in the basin and the thickness can reach up to 4000-6000 meters, but the generation of hydrocarbons appears to be low. Research in this area has focused on studying the oil-generating beds. Among these, the most meaningful research is whether the upper Paleozoic strata exist, and the thickness of this section, which has a decisive meaning to the evaluation of potential oil and gas resources in basin W. In the event that this section exists as we find in the northern oil field, coal-bearing formations may have developed, meaning that oil and gas may have been generated as well. The issue of whether the upper Paleozoic strata exist in this section has been debated over a long period of time. In recent years, more and more evidence has shown that there are upper Paleozoic strata. The main evidence is as follows: 1) A geological survey indicated that the upper Paleozoic is found not only in the northern uplift areas, but also in the southern Qinling Mountain areas. 2) Carboniferous strata were drilled by well W3 in basin W. Accordingly, the consensus has been reached that basin W contains at least some thickness of upper Paleozoic strata. We believe that small-scale non-seismic exploration, performed at a low-cost, can be relied upon to delineate the upper Paleozoic.
- Asia > China > Shanxi Province (0.24)
- Asia > China > Shaanxi Province (0.24)
- Asia > China > Gansu Province (0.24)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Gravity Surveying (1.00)
- Asia > China > Shanxi > Ordos Basin (0.99)
- Asia > China > Shaanxi > Ordos Basin (0.99)
- Asia > China > Qinghai > Qaidam Basin (0.99)
- Asia > China > Gansu > Ordos Basin (0.99)
Summary Many oilfields are in the development stage at present, it has important significance to monitor oil and gas reservoirs using time-lapse electromagnetic. Four continuous electromagnetic profiling (CEMP) lines with 199 stations have been acquired in the Sebei gas field. Remote reference is used to suppress noise during field data acquisition. Data format of 4 frequencies per octave is output to improve the resolution and information of the filed data. High precision two-dimensional magnetotelluric (MT) inversion is adopted to process the data. The inversion results show that, the resolution has been improved with high sampling rate of CEMP data and high precision processing method. It provides technical support for time-lapse MT monitoring. The high resistance characteristic is very clear shown in the inversion profiles of Sebei gas filed. It provides important basis for time-lapse MT monitoring oil and gas reservoir. The inversion results are consistent with the logging curves, and the predicted reservoir area and thickness are consistent with the known wells and the collected data.
- Europe > Norway > Norwegian Sea > Vøring Basin > License 218 > Block 6707/10 > Aasta Hansteen Field > Luva Field > Nise Formation (0.99)
- Europe > Norway > Norwegian Sea > Vøring Basin > License 218 > Block 6706/12 > Aasta Hansteen Field > Luva Field > Nise Formation (0.99)
- Europe > Norway > Norwegian Sea > Vøring Basin > License 218 B > Block 6707/10 > Aasta Hansteen Field > Luva Field > Nise Formation (0.99)
- (2 more...)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Cross-well tomography (0.71)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (0.49)
Summary Alluvial fans are widely developed in the mountain area T, western China. The coarse conglomerate masses in these alluvial fans cause high-velocity anomalies, which are difficult to identify with seismic data. These anomalies make building velocity models more difficult, and limit the ability to predict the locations of traps in the area. So identifying the alluvial fans and understanding the variation of inner lithology and lithofacies are very important for the exploration in the piedmont area. Traditionally, the sedimentary facies are interpreted based on the logging data from individual wells and seismic sequence knowledge, but it is difficult to identify the high-velocity conglomerate mass with seismic data, and to form the tie-well section only with the sedimentary facies interpretation from these wells. EM is a method that are sensitive to lithology variation, but it has low resolution along the vertical direction. In this paper, the EM and seismic methods are combined to interpret the lithofacies of Cenozoic formations and describe the sedimentary lithofacies with the reference of logging data. In the area T, the integration of EM and seismic data, as well as logging data, improves the precision of trap prediction and consequently decreases the drilling risk. The subsequent drilling figures prove the correctness of the lithology prediction, and the misfit between the interpreted depth of target layer and the real figure is decreased to less than 3%.
- Asia > China (0.49)
- Africa > South Africa > Western Cape Province > Indian Ocean (0.41)
- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.99)
- North America > United States > Louisiana > China Field (0.95)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Investigate the Distribution of Conglomerate Masses and Build the Velocity Model with Integrated Gravity, Magnetic, Electromagnetic and Seismic Data: An Example from Kuche Depression, China
Dechun, Li (BGP/CNPC) | Zhi, Zhao (BGP/CNPC) | Zuzhi, Hu (BGP/CNPC) | Shujiang, Yang (BGP/CNPC) | Haiying, Liu (BGP/CNPC)
Summary Conglomerate masses, which widely developed in the piedmont area of Kuche Depression, Northwestern China, seriously affect the seismic structure imaging and limit the seismic interpretation precision. As a result, in a deep reservoir well in the area, the error between the predicted reservoir depth and the real one even is up to about 1000 m. It impedes the progress of hydrocarbon development in the depression. Gravity, magnetic and electromagnetic (EM) responses, especially EM data, are sensitive to the existence of conglomerate masses. An integrated 3D survey including gravity, magnetic and EM methods is conducted in Kuche Depression. All available geophysical data are jointed together and interpreted on an integrated interpretation platform GeoEast®. With the inverted 3D resistivity data we describe the spatial distribution of lithology and with the seismic data we give a believable structural frame interpretation. Joining the two kinds of data as well as drilling data and outcrops information, the distribution of conglomerate masses and their litholohy and lithofacies are comprehensively predicted. Subsequently, we conduct the joint inversion of gravity, magnetic, EM and seismic data and get the structural model of the intermediate and the shallow layers with which we can describe the distribution of conglomerate masses. The work improves the precision of the deep structural trap prediction. Three wells drilled subsequently shows that the maximum error between the predicted gross thickness of conglomerate masses and the real figure is only 3%. The velocity model is also built and used in the subsequent pre-stacking depth migration. It effectively helps limiting the interpretation error which results from the high-velocity horizons. The statistics of drilling data in the area shows that the general misfit of the predicted structure depth decreases from the original about 300 to 1,000 m to the present 30 to 120 m. After the project, a complete joint interpretation system integrating gravity, magnetic, EM and seismic data is released. The system makes the interpretation of multiple geophysical data changing from the traditionally qualitative comparison job to the presently accurate description based on the multiple geophysical data. The quantitative data output from the platform can be used to build velocity model.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
Summary In geologically complex piedmont areas, coarse gravels are richly developed at the bases of alluvial fans, which are difficult to distinguish from arenaceous mudstones in seismic reflection sections. Due to this, it is common to ignore layers with high velocity during velocity modeling in these areas, resulting in lower precision of deep-seated structures with pre-stack depth-migration seismic sections. On the contrary, there is an obvious resistivity contrast between arenaceous mudstones and gravels. Therefore, using MT data can be very beneficial in the identification of locally developed gravels. In this paper, application of MT exploration in the BD area in western China is introduced to effectively determine the distribution of large-scale gravels with MT data as well as its role and significance in identifying lithologies in geologically complex areas.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.57)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.45)
- Geophysics > Seismic Surveying > Seismic Processing (0.71)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.70)
Summary The Simulated Annealing (SA) algorithm has been widely used in geophysical data processing. However, the serial SA algorithm is limited by low computing efficiency. In this paper, the annealing schedule has been improved in the SA algorithm, and a constrained inversion of magnetotelluric (MT) data using the parallel SA algorithm has been applied. The test of synthetic data shows that the improved Very Fast Simulated Annealing (VFSA) method is significantly better than the traditional method. Additionally, the computing efficiency has been improved using the parallel SA inversion algorithm. This method has also been applied in MT data processing for geothermal exploration and good geological results have been achieved. Introduction Inversion of MT data has been gaining increasing attention due to its wide applications in petroleum, mining and geothermal exploration as well as in environmental geophysics and deep-Earth studies (He et al.,2002). Most of the inversion methods are linearized inversion, such as Gauss-Newton, Marquardt-Levenberg and singular-value decomposition. However, these algorithms always converge to local minimum solutions. With the development of the global optimization method, the nonlinear inversion method, especially the SA, has been widely used in geophysical data processing, such as seismic reflection tomography, gravity, DC geoelectrical, NMR, airborne EM inversion and MT data inversion (Yin and Hodges, 2007). Presently, the SA algorithm for MT data inversion is implemented on low-efficiency single PC, and the articles based on parallel computation for MT data inversion are not commonly available. In this paper, we will improve the SA algorithm and apply the parallel SA to a constrained inversion of MT data for geothermal exploration.. Method Simulated Annealing (SA) was originally proposed by Metropolis in 1953. Kirkpatrick introduced this method to solve global optimization problems in 1983. In 1989, Inger developed the Very Fast Simulated Annealing (VFSA) based on SA, which included the model selection and perturbation, the objective function, the annealing schedule and acceptance probability. All the parallel inversions with different numbers of processors have achieved the desired misfit of the objective function. The average results of the parallel inversion are almost the same as the true model parameters, which proves the validity of the parallel algorithms. Table 1 shows that with the same simulated annealing parameters, the average number of inversion iterations gradually reduces and the average time-consumption for computation also gradually decreases with increasing processors. The average inversion iteration number with 1 processor is 344 and decreases to 187 when 32 processors are used. The average iteration inversion time with 1 processor is 0.529s and decreases to 0.399s when 16 processors are used. However, the communication time between processors increases with the increasing number of processors for calculation, so the average time-consumption for inversion with 32 processors in this program is slightly more than that with 16 processors. Application The work area is located in western Hungary. Two sets of geothermal reservoirs are developed in the work area: shallow Middle and Upper Pliocene sandstone and a deeper Triassic reservoir composed of carbonate rocks.
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Non-Traditional Resources > Geothermal resources (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Cross-well tomography (1.00)
Deep Structure Study In Complex Area By 3D MT Data
Weibin, Sun (BGP, CNPC) | Yuyou, Deng (BGP, CNPC) | Caifu, Wang (BGP, CNPC) | Zuzhi, Hu (BGP, CNPC) | Cunguo, Lin (BGP, CNPC)
Summary Block SZG is located in the western China and the topography is undulated sharply. Complex terrain makes deep seismic data poor and interpretation difficult. It is the first time to carry out 3D MT survey in the area for the purpose of assisting seismic survey to study deep structure. Compared with traditional 2D MT survey, 3D MT has more advantages on static correction, processing and inversion algorithms, and gives a technical basis to the study of spatial distribution of underground resistivity in complex area. With the calibration of logging data, after mapping the burial depth to the top resistive basement based on 3D inversion resistivity, the structure of top basement can be interpreted. The case in the area shows that it is feasible to study deep structures in complex area by 3D MT data and the application effect is also described in the following paragraphs. Introduction Block SZG is located on the southwest margin of folded thrust belt YXL in West Depression of Qadam basin. In the area, 70% of the surface is covered by mountains. The special tectonic background makes topographic surface sharply undulated. Rugged topography and land feature among with complex tectonic characteristics reduce the imaging accuracy and reliability of seismic data related with deep structures, and the exploration work is limited. Well S35 in the area discovers the structure peak different from seismic prediction (Figure 1). Therefore, the oil company recognizes that solo seismic survey limits the study of deep structure and decides to deploy 3D MT survey in the area. 3D MT survey covers an area of 405km2 with total 70 lines with a station space of 500m and a line space of 500m. Its geology goals are to map the top Paleozoic basement and further infer the potential areas. In the area, Quaternary and Upper Tertiary rock outcropped on the ground, and in the mountain area the later appears mainly. According to the electric logging data and physical property determination to outcrop samples, the Quaternary formation is a high resistivity layer, mainly composed of gravel with the logging resistivity of higher than 10 ohm·m. Upper Tertiary and later Lower Tertiary are low resistivity layers, mainly composed of sandstone, mudstone and sandwich of mudstone and sandstone with a resistivity of about 5 to 8 ohm·m. Early Lower Tertiary is mainly composed of sandy mudstone with logging resistivity of about 15 ohm·m. The basement composed of Paleozoic metamorphic rock is the resistive basement with the outcrop resistivity of thousands ohm·m. Feasibility of studying deep structure by MT data To analyze the efficiency of MT survey on the prospecting of deep structure in complex area, a geo-electrical model is designed as figure 2a according to the topography in the area. In the model, the topography is presented by several blocks with different resistivity. The resistive block is 2km wide and 0.6km thick and its resistivity is 100 ohm·m. The conductive block is an outcropped sedimentary rock with resistivity of 10 ohm·m and the width of 2km.
- Asia > China (0.49)
- Europe > Norway > Norwegian Sea (0.25)
- North America > Canada > Alberta > Woodlands County (0.25)
- Phanerozoic > Cenozoic > Quaternary (0.75)
- Phanerozoic > Cenozoic > Paleogene (0.46)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.66)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.45)