四川盆地开江地区WT1井震旦系陡山沱组物源分析及构造背景判断*

为厘清四川盆地开江地区WT1井震旦系陡山沱组地层发育的构造背景,重塑其原型沉积盆地,为重建古地理及油气进一步勘探部署工作提供科学依据,利用岩石学、岩石地球化学及碎屑锆石U-Pb年代学手段对该井陡山沱组物源进行了分析。岩石地化结果表明WT1井陡山沱组沉积物源岩为中基性火山岩类且经历了中等程度化学风化作用;碎屑锆石U-Pb年龄主要记录了915~850Ma、794~714.5Ma以及622~700Ma 3个阶段的构造岩浆活动,与扬子克拉通北缘及邻区Rodinia超大陆的裂解演化过程有关,该构造活动背景下的中基性火山岩是WT1井陡山沱组沉积的主要物质来源。结果表明四川盆地开江地区的陡山沱组发育于拉张伸展构造背景,属克拉通内裂陷盆地沉积,有利于烃源岩层的发育;同时为Rodinia超大陆的裂解演化过程提供了重要的年代学演化证据。该成果对深入认识四川盆地的地质结构、沉积-构造演化以及油气勘探的战略部署等具有极为重要的科学意义。     

四川盆地东部中二叠统茅口组热液活动特征

四川盆地中二叠统茅口组存在丰富的热液流体活动。在野外剖面和岩心观察的基础上,利用多种分析测试方法,对四川盆地东部茅口组碳酸盐岩中的热液活动特征进行了探讨。研究区茅口组热液活动包括2类,一类为硅质热液,表现为薄层硅质岩或硅质团块;另一类为碳酸盐型热液,表现为粗晶方解石和白云石充填在张性构造裂缝中或呈"雪片状"集合体,偶见少量黄铁矿、闪锌矿等金属硫化物伴生。硅质岩主量元素之间的关系表明硅质来源具有多样性,是热液硅与壳源硅的混合;黄铁矿、闪锌矿原位S同位素δ34S介于-3.91‰~-6.87‰之间,推测可能受到了微生物和基性岩浆岩的双重影响;方解石脉和具鞍状双晶的白云石脉普遍具有Ca O高于标准计量、相对富Sr和U、贫REE和Ti、Ce/Ce*明显负异常、Eu/Eu*主体正异常、Y正异常等特征,表明碳酸盐脉为热液活动的产物,流体来源也呈现出火山活动相关流体与富钙地下水混合的特点。闪锌矿富集Ge和Cd,Zn/Cd及Zn/Fe值反映了中等成矿温度。硅质岩和碳酸盐流体包裹体均一温度变化范围较大,介于54.7~294.3℃之间,与稀土元素和微量元素比值所揭示的特征吻合。研究结果表明,穿层状硅质结核和团块的首次出现往往标志着茅口组热液活动的开始,而张性构造裂缝和碳酸盐型热液活动带来的围岩白云化作用则有效地改善了白云岩储集层的物性。     

二叠系火山碎屑岩气藏天然气地球化学特征及气源分析——以四川盆地成都—简阳地区永探1井为例

2018年12月底,在四川盆地二叠系火山岩发育地区部署的风险探井永探1井测试获得22.5万m³/d的高产工业气流,实现了二叠系火山碎屑岩的重大勘探突破,展现了火山碎屑岩气藏的巨大勘探潜力。本文根据该钻井成果资料,应用分析化验数据,采用不同地区气-气对比方法,开展了成都—简阳地区天然气地球化学特征分析、源-储接触关系解剖及气源综合对比。在此基础上,对二叠系火山碎屑岩气藏天然气成因进行了鉴别,对天然气来源进行了判识。结果表明：永探1井火山岩气藏天然气均为典型的干气,干燥系数达0.997,CH₄质量分数高,平均为99.03%,C₂H₆质量分数低,平均为0.35%,C₃H₈几乎没有,不含H₂S;天然气甲烷、乙烷碳同位素值均较轻,甲烷碳同位素平均值为-32.17‰,乙烷碳同位素平均值为-34.27‰,且岩心及镜下可见大量残余沥青,天然气成因鉴别分析表明永探1井火山岩气藏天然气为原油二次裂解气;通过源-储接触关系解剖以及天然气甲烷、乙烷碳同位素对比分析,表明永探1井火山碎屑岩气藏天然气主要来源于德阳—安岳台内裂陷内发育的巨厚下寒武统筇竹寺组优质烃源岩,部分来源于中二叠统。     

基于欧拉反褶积法的无人机航磁找矿应用

无人机航磁测量以其高效、安全、低成本等优点逐渐应用到中小面积大比例尺资源勘探领域。本文针对安徽芜湖市三山区22 km²区域构造刻画和找矿前景预测,开展1∶20 000高精度无人机航磁测量工作;推导了欧拉反演求解过程,并分析对比构造指数选取,使用构造指数为0的欧拉反褶积对航磁数据进行反演求解,获得地下磁异常体构造边界解集信息;将其应用到研究区域航磁数据反演,根据反演得到的边界位置和深度信息圈定了18处断裂,并结合异常特征给出了8.7 km²岩浆岩分布区,根据宁芜区域铁矿成矿特点,圈定岩浆分布区及边界为成矿详查区。     

上扬子地区不同类型岩石生氦潜力评价及泥页岩氦气开采条件理论计算

上扬子地区是我国页岩气重要开采区,也是氦气工业性开采的唯一地区,但对于其氦气生成潜力研究仍处于空白阶段.据此,对上扬子东南地区采集的144件岩石样品进行场发射扫描电镜及铀、钍强度测试,理论计算了页岩气中氦气达到我国工业开采标准(0.05％)需要满足的理论条件.岩石扫描电镜结果表明,富含铀、钍的副矿物(锆石、独居石、铀钍石及磷灰石等)主要赋存于造岩矿物石英和长石中.岩石铀、钍强度测试结果表明,三大类岩石中铀平均含量呈现:沉积岩(8.96×10-6)＞岩浆岩(4.83×10-6)＞变质岩(1.89×10-6);钍平均含量呈现:沉积岩(11.01×10-6)≈变质岩(10.4×10-6)＞岩浆岩(5.9×10-6).岩浆岩中铀、钍平均含量呈现:酸性岩＞中性岩＞基性岩＞超基性岩;沉积岩中铀平均含量呈现泥页岩(13.86×10-6)＞＞砂岩(2.54×10-6)＞碳酸盐岩(1.67×10-6);钍平均含量呈现泥页岩(12.50×10-6)≈砂岩(12.76×10-6)＞碳酸盐岩(5.96×10-6).不同沉积时代沉积岩中铀、钍平均含量也呈现上述分布规律.沉积岩中铀、钍含量主要与岩石的沉积环境与物源有关,与沉积时代无关.单位时间、单位质量岩石氦气生成量的大小为:泥页岩＞酸性岩＞中性岩＞砂岩＞变质岩＞碳酸盐岩＞基性岩＞超基性岩.以中国南方地区上奥陶统五峰组—下志留统龙马溪组页岩为例,当该层位泥页岩中残余氦气含量为U,Th元素衰变释放出来氦气含量的80％以上时,适合进行"页岩气+氦气"的共同开采,提高页岩气的开采价值.     

煤炭地质勘查产业转型发展的思考与探讨

煤炭地质勘查工作关系国家能源安全,在国民经济发展中具有重要的战略地位。伴随煤炭行业绿色转型和新能源革命新形势,及时调整产业结构,向大地质、大资源、大生态方向拓展服务领域,为国家生态文明建设和煤炭产业绿色安全高效发展提供坚实的地质技术保障,成为煤炭地质勘查产业转型发展必然趋势和未来发展方向。结合煤田地质勘查工作发展历程及现状、发展趋势,对煤炭地质勘查产业转型发展方向进行深入剖析和探讨。     

Field study on the behavior of pre-bored grouted planted pile with enlarged grout base

This paper presents the results of field tests performed to investigate the compressive bearing capacity of pre-bored grouted planted (PGP) pile with enlarged grout base focusing on its base bearing capacity. The bi-directional O-cell load test was conducted to evaluate the behavior of full scale PGP piles. The test results show that the pile head displacements needed to fully mobilize the shaft resistance were 5.9% and 6.4% D (D is pile diameter), respectively, of two test piles, owing to the large elastic shortening of pile shaft. Furthermore, the results demonstrated that the PHC nodular pile base and grout body at the enlarged base could act as a unit in the loading process, and the enlarged grout base could effectively promote the base bearing capacity of PGP pile through increasing the base area. The normalized base resistances (unit base resistance/average cone base resistance) of two test piles were 0.17 and 0.19, respectively, when the base displacement reached 5% D_b (D_b is pile base diameter). The permeation of grout into the silty sand layer under pile base increased the elastic modulus of silty sand, which could help to decrease pile head displacement under working load.

     

Carboniferous ridge subduction in the Xingmeng Orogenic Belt: Constraints from geochronological,geochemical, and Sr-Nd-Hf isotopic analysis of strongly peraluminous granites and gabbro-diorites in the Xilinhot micro-continent

The Xingmeng Orogenic Belt evolved through a long-lived orogeny involving multiple episodes of subduction and accretion. However, there is a debate on its tectonic evolution during the Late Paleozoic. Here, we report geochemical, geochronological, and isotopic data from strongly peraluminous granites and gabbro-diorites from the Sunidzuoqi–Xilinhot region. Zircon U–Pb ages suggest that the intrusive rocks were emplaced during the Early Carboniferous (333–322 Ma). The granites exhibit geochemical characteristics similar to S-type granites, with high SiO₂ (72.34–76.53 wt.%), Al₂O₃ (12.45–14.65 wt.%), and A/CNK (1.07–1.16), but depleted Sr, Nb, and Ta contents. They exhibit positive ε_Nd(t) and ε_Hf(t) values (?0.3 to 2.8 and 2.7–5.7, respectively) and young Nd and Hf model ages (T_DM2(Nd)=853–1110 Ma and T_DM2(Hf)=975–1184 Ma), suggesting that they may be the partial melting products of heterogeneous sources with variable proportions of pelite, psammite, and metabasaltic rocks. The meta-gabbro-diorites from the Maihantaolegai pluton have low SiO₂ (47.06–53.49 wt.%) and K₂O (0.04–0.99 wt.%) contents, and demonstrate slight light rare earth element (REE) depletion in the chondrite-normalized REE diagrams. They have high zircon ε_Hf(t) values (14.41–17.34) and young Hf model ages (T_DM2(Hf)= 230–418 Ma), indicating a more depleted mantle source. The variations of the Sm/Yb and La/Sm ratios can thus be used to assess the melting degree of the mantle source from 5% to 20%, suggesting a quite shallow mantle melting zone. We propose that the petrogenesis and distribution of the strongly peraluminous granites and gabbro-diorites, as well as the tectonic architecture of the region, can be explained by a ridge subduction model. Based on these results, and previous studies, we suggest a southward ridge subduction model for the Sunidzuoqi–Xilinhot region.     

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.     

Machine learning reveals the influences of grain morphology on grain crushing strength

Acta Geotechnica - Grain morphology has significant impacts on the mechanical behaviors of granular materials. However, its influences on grain breakage are still poorly understood due to the...