地电井下观测装置技术指标的测试与认定讨论——井下观测设施构建与测试技术

观测技术系统的设施构建技术指标认定是观测技术的核心所在,本文结合地电井下观测设施构建工程^[1],着重讨论针对观测装置测试的需求、技术指标测试的方法以及认定的过程。文中以具体工程参数为例,给出4项测试结果,讨论了认定依据和认定过程,希望本讨论能对地电井下观测设施建设、指标的认定起到参考作用。     

甘肃武威盆地页岩气“武页1井”成膜低固相冲洗液的应用

“武页1井”是甘肃省地质调查院实施的一口页岩气基础地质条件调查井,主要目的是为了查明页岩层段的分布。地层岩性主要为泥岩、砂岩、页岩、碳质板岩、灰岩。浅层倾角大、胶结性差、破碎、井壁强度低。现场采用成膜低固相冲洗液体系,解决了地层破碎、坍塌掉块和水敏性钻探施工难题,体系具有护壁性、抑制性和胶结性。使用中取得了较好的效果,保证了该项目的顺利施工。     

井筒声场研究回顾与展望

声波测井是测井学科的三大方法之一,在储层评价中起到了重要作用．随着陆上油气勘探目标趋向孔隙结构复杂、非均质性强的油气储集层,基于地层各向同性介质的测井理论显露弊端;因此研究复杂地质条件下的井筒声场分布特征、为储层解释和发展测井新方法提供理论依据成为必然．本文对井筒声场研究进行了回顾,并就当前井筒声场研究现状进行了总结,对其发展趋势和应用前景进行了分析。     

对巨厚粉砂层中使用CFG桩有关问题的探索和研究

通过工程实例,介绍了在巨厚粉砂层中使用CFG桩处理地基存在的一些问题以及注意事项,阐述了一些常见事故及预防措施,特别提出了为保证基坑边坡稳定所采取的空桩处理措施方法。     

超长细桩成孔垂直度施工控制

通过天津高银商务区117主塔楼基础桩施工实践,就超长细桩成孔垂直度控制问题进行试验、研究,提出一些具体的控制措施,总结了施工经验和体会。     

井孔声场的本征模及其在分层介质井孔声场计算中的应用

研究无穷大介质中的井孔本征模问题．从有限半径的波导出发,分析半径无限增大的极限情况,证明无穷大介质中的井孔有离散和连续两类本征模,组成正交完备基．进而应用它们分析计算分层介质中井孔的声场,算得孔外分层介质在孔内产生的反射波．     

微生物-CMC无固相钻井液固壁作用与机理初探

松散破碎性地层孔壁失稳一直是困扰钻探工程界的难题之一,增强该类地层的胶结性,提高其力学性能是有效解决孔壁失稳的技术关键。本文将微生物诱导碳酸钙沉积（MICP）技术与CMC无固相钻井液相结合,构建微生物-CMC无固相钻井液体系。通过岩心浸泡实验、X射线衍射实验（XRD）以及扫描电镜分析两种微观分析手段对微生物-CMC无固相钻井液的固壁作用与机理进行了初探。结果表明：微生物-CMC无固相钻井液对松散破碎性地层具有较明显的加固作用,且作用时间越长,初始菌种浓度越高,钙源浓度越大,固壁效果越好。在固壁过程中,微生物随钻井液渗透进入试样内部,在松散颗粒之间诱导生成碳酸钙晶体,填充孔隙空间,将松散颗粒胶结成整体,并具有一定的力学强度,从而达到加固孔壁的目的。本研究结果为解决松散破碎性地层孔壁失稳提供了新的钻井液技术方案。     

金属套管井中电磁测井响应的数值计算

分析了金属套管井中有限尺寸线圈的电磁响应特征,讨论了井眼泥浆参数、套管参数和不同线圈尺寸对电磁测井响应的影响,考察了金属套管中电磁测井对地层电阻率变化的灵敏度.径向非均匀介质的模拟计算表明,在套管井中井眼泥浆对电磁测井的影响较大,必须进行校正.在低频条件下,井间测量响应中金属套管与地层的电磁响应是非耦合的,两者可以分离.在套管参数一定时,套管响应与地层的响应的比值近似一个常数.电磁响应对地层电阻变化的灵敏度分析显示,井间电磁测量在储层研究尺度对地层电阻率变化响应灵敏,套管引起的衰减稳定.在其它参数相同的前提下,线圈直径的增大有助于有效电磁响应信号的增强,给大井间距测量提供了可能.不同磁导率金属套管的电磁响应分析表明,电磁测井中,采用非磁性或弱磁性套管要优于一般的铁磁性套管.     

Climate change of the last millennium inferred from borehole temperatures: regional patterns of climatic changes in the Czech Republic — Part III

Accurate temperature–depth profiles may help to assess the temperature variations associated with the climate changes in the past. Ninety-eight ground surface temperature histories inverted from the temperature–depth borehole logs drilled on the territory of the Czech Republic [Bodri, L., ermák, V., 1995. Climate changes of the last millennium inferred from borehole temperatures: results from the Czech Republic — Part I. Global Planet. Change 11, pp. 111–125; Bodri, L., ermák, V., 1997. Climate changes of the last two millennia inferred from borehole temperatures: results from the Czech Republic — Part II. Global Planet. Change 14, pp. 163–173.] are used to reconstruct the regional patterns of the respective climate change. The climate was mapped for the following periods: 1100–1300 A.D. (Little Climatic Optimum), 1400–1500 A.D., 1600–1700 A.D. (main phase of the Little Ice Age), and for the most recent climate trend after year 1960. Comparison of the obtained maps with the meteorological observations and proxy climatic reconstructions confirmed good applicability of the “geothermal” paleoclimatic reconstructions for the regional studies.     

Inference of in situ stress from thermoporoelastic borehole breakouts based on artificial neural network

The determination of in situ stresses is very important in petroleum engineering. Hydraulic fracturing is a widely accepted technique for the determination of in situ stresses nowadays. Unfortunately, the hydraulic fracturing test is time-consuming and expensive. Taking advantage of the shape of borehole breakouts measured from widely available caliper and image logs to determine in situ stress in petroleum engineering is highly attractive. By finite element modeling of borehole breakouts considering thermoporoelasticity, the authors simulate the process of borehole breakouts in terms of initiation, development, and stabilization under Mogi-Coulomb criterion and end up with the shape of borehole breakouts. Artificial neural network provides such a tool to establish the relationship between in situ stress and shape of borehole breakouts, which can be used to determine in situ stress based on different shape of borehole breakouts by inverse analysis. In this paper, two steps are taken to determine in situ stress by inverse analysis. First, sets of finite element modeling provide sets of data on in situ stress and borehole breakout measures considering the influence of drilling fluid temperature and pore pressure, which will be used to train an artificial neural network that can eventually represent the relationship between the in situ stress and borehole breakout measures. Second, for a given measure of borehole breakouts in a certain drilling fluid temperature, the trained artificial neural network will be used to predict the corresponding in situ stress. Results of numerical experiments show that the inverse analysis based on finite element modeling of borehole breakouts and artificial neural network is a promising method to determine in situ stress.