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The factors controlling the migration and enrichment of iron in Archean banded iron formations are important for understanding the formation of iron ore resources. Here we present, for the first time, a micro-three-dimensional modeling method that can be applied to hand specimens of iron ores. This method and petrographic observations, electron probe microanalysis, and the structural deformation characteristics of magnetite quartzite are used to assess the mechanisms of iron enrichment in such ores. The Anshan–Benxi area is located in the eastern part of the North China Craton, and is one of the most significant Archean banded iron deposits in the world. Magnetite quartzite is developed on a large scale in this area, which has experienced complex structural deformation, and thus these samples are ideal for our study. As such, we undertook micro-scale, three-dimensional structural modeling of representative hand specimens of the magnetite quartzite. Our methodology involved combined surface and internal modeling. The rock samples were sliced at fixed intervals and detailed scanned images of the slices were obtained, which enabled a three-dimensional model to be constructed of each sample. These three-dimensional models allow the deformational characteristics of the banded iron formation to be quantified on a hand specimen scale. Petrographic analysis showed that the banded magnetite quartzite has experienced brittle deformation, and electron microprobe analysis revealed iron enrichment in the fold axes. Structural deformation, such as folding and ductile shearing, has a controlling effect on iron enrichment, and the microscopic three-dimensional modeling method provides a new approach for studying the deformational characteristics of iron-bearing formations.

Lack of knowledge of the basement structure of the Paleoproterozoic Jiao–Liao–Ji Belt (JLJB) of the North China Craton has hindered understanding of the tectonic evolution of the belt and, to an extent, the entire craton. Previous studies of the JLJB were based mostly on petrological, structural, and geochemical analyses, resulting in a lack of understanding of the deep geological structure of the belt. In this study, we used a combination of geophysical and geological methods to interpret the deep geological structure of the JLJB on the basis of high-precision non-seismic geophysical data. We collected high-precision gravity, magnetic, and magnetotelluric sounding data. We also compiled data on rock physical parameters for important geological bodies, and obtained high-precision Bouguer gravity anomaly data from the China Geological Survey. We interpreted these data to visualize the subsurface distributions of geological bodies (including sedimentary and metamorphic units and intrusive rocks) in the Kuandian–Huairen area of the JLJB. We found that the Liaoji granites are developed on a large scale in the deep part of the JLJB, showing a weak gravity anomaly (−25 to −6 mGal) and high resistivity (103.5 to 106 Ω·m). These granites may have intruded along a series of normal faults at the time of the initial formation of the belt, and expanded over time. Subsequently, the low-resistivity Liaohe Group (10 to 102.5 Ω·m) was deposited on the Liaoji granite across a sedimentary contact. Therefore, the Liaoji granite forms the basement of the JLJB. The presence of extensional faults in the deep part of the JLJB suggests that the belt was also subjected to tectonic extension. The Yuejiadian–Xiaohuanggou fault to the east of Kuandian may be the boundary between the South Liaohe Group and the North Liaohe Group. The South Liaohe Group is relatively thin and tightly folded, whereas the North Liaohe Group is thicker and characterized by relatively open folds.

The Liaodong area which lies in the Liaoji Belt of northeastern China is rich in geothermal resources, but locating the resources is challenging. Here non-seismic geophysical data, existing geological data and the physical properties of rocks are examined to locate and characterise potential geothermal resources. High-precision gravity, aeromagnetic and magnetotelluric data are analysed to determine the characteristics of the hidden faults in the region and map the spatial distribution of the subsurface rock mass and its properties. The distribution of hidden faults and the basic characteristics of the rock mass were determined via Euler deconvolution of the gravity and aeromagnetic data. The subsurface extension of concealed faults, the spatial distribution of intrusive rocks, and the specific locations of underground thermal storage structures were determined from magnetotelluric data analysis, thereby enabling geothermal energy targets to be identified. The results highlight four potential sites of geothermal energy development in the study area, which indicate that non-seismic geophysical data can provide reliable clues to the geothermal energy potential in a given region.

Oil and gas reserves in sand-shale thin interbeds are extensive, but it is a challenge to achieve satisfactory precision in the identification of these interbeds using traditional logging data. Phasor induction and three-component induction logging are appropriate tools for the identification of sand-shale interbeds. Unfortunately, phasor induction data are expensive and three-component induction logging is rarely used in major domestic oil fields. As a result, evaluation of sand-shale thin interbeds heavily depends on a traditional logging suite, general image logging, array lateral logging and array induction logging, and so on. In this paper, we investigate the sand-shale thin interbed region in the northern part of the Sulige field. We propose a comprehensive method of anisotropic evaluation which is based on the combination of wellbore microresistivity image logging (fullbore formation microimage (FMI)) and high-resolution array induction logging (HRLA), where HRLA can be used instead of a traditional logging suite or high-resolution array lateral logging. The proposed method works well in the evaluation of thin interbeds in the northern part of the Sulige field. In this paper, the combination of FMI and deep resistivity of HRLA is used. It should be noted that it is possible to use deep resistivity of conventional laterolog or array induction log instead of HRLA.

The tectonic evolution of the Paleoproterozoic Jiao–Liao–Ji Belt, which is located in the North China Craton in northeastern China, is the subject of ongoing debate. Most previous studies on this belt have been based on traditional geological methods, with most of the data being obtained from the surface, leading to uncertainties regarding the deep geological structure of the belt. This has restricted our understanding of the tectonic character and development of the belt; however, this limitation could be overcome by geophysical studies. Here, we analyze and interpret newly collected rock physical property data (density, magnetic susceptibility, and resistivity) and non-seismic geophysical data (gravity, magnetic, and magnetotelluric sounding data) for the Lianshanguan area to infer the location, configuration, and nature of the tectonic boundary between the Paleoproterozoic Jiao–Liao–Ji Belt and the Archean Longgang Block. The Lianshanguan rock mass, which belongs to the Archean Longgang Block, shows a weak gravity anomaly (0–6 mGal) and high resistivity (103.6 to 104 Ω·m). The area south of the Lianshanguan rock mass consists of the Paleoproterozoic Liaohe Group and displays an intermediate magnetic anomaly (0–200 nT) and low resistivity (10–102.5 Ω·m). We therefore infer that the tectonic boundary of the Jiao–Liao–Ji Belt is located along the southern margin of the Lianshanguan intrusion. We also show that the tectonic boundary in the Eastern Liaoning–Southern Jilin area trends E–W and extends from Shuangtaling through Lianshanguan, Caohecheng, Balidianzi, and to Guaimozi. The boundary between the Longgang Block and the Jiao–Liao–Ji Belt dips predominantly to the south, suggesting an extensional tectonic regime during the Paleoproterozoic time.

Tight sand gas reservoirs are our country’s fairly rich unconventional natural gas resources, and their exploration and development is of prime importance. Sulige Gas Field which located in the northern Ordos Basin is tight sand gas reservoirs. It is typically featured by low porosity and low permeability, and the error of porosity calculation by traditional methods is larger. Multicomponent explanation model is built by analyzing the thin slice data, and the objective function is got according to the concept of optimization log interpretation method. This paper puts the Genetic Algorithm and the Complex Algorithm together to form the GA-CM Hybrid Algorithm for searching the optimal solution of the objective function, getting the porosity of tight sandstone gas reservoirs. The deviation got by this method is lesser compared with the core porosity, with a high reliability.

With the characteristics such as low porosity, low permeability and low gas saturation, tight sandstone reservoirs almost have no natural capacity and need to be fractured (or fracturing) for productivity. Therefore, fracturing capacity prediction is very necessary. However there were so many factors, and the relations between the factor and the post-frac productivity are complex. In this study, first of all factors are concluded from the gas well stable productivity formula, then I used factor analysis to look for the main factors from the logging parameters and fracturing parameters in SULIGE area. This study could provide basal references for the gas post-frac productivity prediction in tight sandstone reservoirs.

Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea. In this paper, we first compute the mother wavelet transform of conventional logs and the wavelet decomposed signals are compared with fractures identified from image logs to determine the fracture-matched mother wavelet. Then the mother wavelet-based decomposed signal combined with the differential curves of conventional well logs create a fracture indicator curve, identifying the fractured zone. Finally the fracture density can be precisely evaluated by the linear relationship of the indicator curve and image log fracture density. This method has been successfully used to evaluate igneous reservoir fractures in the southern Songnan basin and the calculated density from the indicator curve and density from image logs are both basically consistent.