200～300N高强度合成金刚石及其在钻探中的应用

对会“打滑”地层,国内外均为难题,新研究的技术是：（１）用特殊的新构思,运用内设备,压制出可批量２供应的高强度金刚石,这种高强度金刚石可承受比目前高得多的钻压;（２）运用强力钻进的配套技术,用上述高强度金刚石制作头。     

硅质碎屑砂岩中胶结物来源的新认识

分析了浅埋藏砂岩的成岩环境,并从岩石学、矿物学、同位素及质量平衡估算方面论证硅质碎屑砂岩中的胶结物主要来源于砂岩浅埋藏期间的地下水,进而分析浅埋藏带内地下水的流动机制及早期胶结作用对晚期成岩作用的影响。     

涠西南地区岩石特性及钻井液技术研究

北部湾盆地涠西南油田群是南海西部重要的原油产区，同时油田群紧邻众多国家级自然保护区，属于环境敏感区域。针对北部湾盆地涠西南地区环保要求，以及涠洲组和流沙港组钻井过程经常出现井壁失稳导致的憋卡、起下钻阻卡等问题，开展了涠西南地区地层泥页岩特性及环保钻井液技术研究。通过地层岩石黏土矿物分析、孔喉结构分析以及理化性能评价，明确了涠西南地区泥页岩井壁失稳的机理；提出以类油基的水溶性复合基基液为核心，构建了一套具有油基钻井液工程特性和水基钻井液环保特性的新型环保防塌钻井液技术，并进行了相关的现场应用。现场应用显示：应用井与邻井相比，12-1/4″井段阻卡划眼时间减少，井径扩大率大幅度降低，并且在128 h的长时间浸泡过程中没有复杂情况产生，有效地解决了涠洲组、流沙港组易失稳地层的井壁稳定问题；同时，钻井液环保性能达到一级海域环保要求，可以替代目前应用的油基钻井液体系，解决了海上使用油基钻井液存在配制成本高、含油钻屑需全回收及环境危害隐患大等技术难题。该研究对于涠西南油田环保、井壁失稳和钻井液技术发展具有较高的理论价值和借鉴意义。     

南海海盆区莫霍面分布规律及其对深部钻探的意义

钻遇莫霍面是人类一直以来的梦想。深海海底是地球上离莫霍面最近的地方，目前有研究推测南海是世界上莫霍面深度最浅的海域之一，但缺乏足够的直接证据。深反射地震探测可以直接揭示岩石圈的构造形态，是莫霍面探测的重要手段。本文基于长达15000 km的深反射多道地震剖面的解释、处理、制图和分析，结合前人的研究，形成了南海海盆区莫霍面反射特征和空间分布的初步认识。① 南海东部次海盆南部早期经历了较快速扩张，岩浆供应充足，受扩张停止后岩浆活动影响较小，基底平坦，地质构造相对简单，同时洋壳地震速度结构不存在异常，且有较强的广角莫霍面反射波和可识别的地幔顶部折射波，具备莫霍面钻探的基本条件。② 南海海盆不同区域的莫霍面反射强度存在较大差异。其中东部次海盆莫霍面反射最为强烈且清晰，西北次海盆次之，西南次海盆仅有零星出现的清晰莫霍面反射且可信度不高。③ 识别南海海盆区莫霍面地震反射长度超过3500 km，首次形成了海盆区深度域莫霍面地震反射空间分布图。与重力反演的莫霍面深度相比，利用深反射多道地震计算的莫霍面深度细节更为丰富，并且可以在垂向上清晰刻画莫霍面的结构。整体上，南海海盆区莫霍面地震反射强烈和可信度高的区域中，深度较浅的区域之一是东部次海盆南部，最浅处仅约9. 5 km，其中水深4. 01 km，洋壳厚度仅5. 54 km。综合判断，东部次海盆南部是南海重要的莫霍面钻探备选区，这对南海莫霍面钻探选址具有重要意义。     

随钻测录井地质导向二维分解实时绘图方法

根据随钻测录井实时地质导向和大斜度、水平井评价成图技术需求，针对传统绘图方法存在的弊端，提出了将测录井信息、井眼轨迹和地质模型进行二维分解的实时绘图方法。针对二维分解绘图方法绘图时空复杂度较高的问题，给出了不同事件驱动下的局部实时计算和拷屏重绘算法，控制了对CPU和内存的消耗、提高了绘图效率，消除了实时绘图的闪烁和卡顿现象。应用实例表明，二维分解实时绘图方法能够实现大尺度随钻测录井地质导向图形的流畅、无卡顿实时绘图，可提高大斜度、水平井储层模型评价的刻画精度和时效。     

CQK-50型车载化钻机的研制

在分析研究小型地质车载钻机的形式、功能与特点的基础上，提出了钻机车载化的新思路。根据地质调查工作现状，结合国内现有小型地质取样钻机，研制了CQK-50型全液压多功能车载取样钻机，并在野外物化探遥感异常验证钻探中取得了良好效果。     

我国地质找矿取心（样）钻探设备现状及提高效能的分析研究

概述了我国地质找矿钻探市场现状及前景，分析研究了地质取样钻探传统的立轴钻机及全液压动力头钻机的优缺点及综合效能，针对国家提出加强地质工作的形势及中国的实际状况，提出了完善和改进我国现有钻探设备、提高我国地质找矿取样钻探效率、降低能耗及成本的观点及建议。     

冲击凿岩钎杆的瞬态动力学分析

以ANSYS为平台，按照真实工作状态对凿岩钎杆进行了瞬态动力学分析，并对不同活塞形状下分析结果数据进行了比较，为冲击凿岩系统设计提供了理论依据。     

Improved reliability in the interpretation of geochemical measurements by the quantification of uncertainty from sampling

All geochemical measurements require the taking of field samples, but the uncertainty that this process causes is often ignored when assessing the reliability of the interpretation, of the geochemistry or the health implications. Recently devised methods for the estimation, optimisation and reduction of this uncertainty have been evaluated by their application to the investigation of contaminated land. Uncertainty of measurement caused by primary sampling has been estimated for a range of six different contaminated land site investigations, using an increasingly recognized procedure. These site investigations were selected to reflect a wide range of different sizes, contaminants （organic and metals）, previous land uses （e.g. tin mining, railway sidings and gas works）, intended future use （housing to nature reserves） and routinely applied sampling methods. The results showed that the uncertainty on measurements was substantial, ranging from 25% to 186% of the concentration values at the different sites. Sampling was identified as the dominant source of the uncertainty （〉70% of measurement uncertainty） in most cases. The fitness-for-purpose of the measurements was judged using the optimized contaminated land investigation （OCLI） method. This identifies the optimal level of uncertainty that reduces to overall financial loss caused by the measurement procedures and the misclassification of the contamination, caused by the uncertainty. Generally the uncertainty of the actual measurements made in these different site investigations was found to be sub-optimal, and too large by a factor of approximately two. The uncertainty is usually limited by the sampling, but this can be reduced by increasing the sample mass by a factor of 4 （predicted by sampling theory）. It is concluded that knowing the value of the uncertainty enables the interpretation to be made more reliable, and that sampling is the main factor limiting most investigations. This new approach quantifies this problem for the first time, and allows sampling procedures to be critically evaluated, and modified, to improve the reliability of the geochemical assessment.     

T -distributed Random Fields: A Parametric Model for Heavy-tailedWell-log Data1

Histograms of observations from spatial phenomena are often found to be more heavy-tailed than Gaussian distributions, which makes the Gaussian random field model unsuited. A T-distributed random field model with heavy-tailed marginal probability density functions is defined. The model is a generalization of the familiar Student-T distribution, and it may be given a Bayesian interpretation. The increased variability appears cross-realizations, contrary to in-realizations, since all realizations are Gaussian-like with varying variance between realizations. The T-distributed random field model is analytically tractable and the conditional model is developed, which provides algorithms for conditional simulation and prediction, so-called T-kriging. The model compares favourably with most previously defined random field models. The Gaussian random field model appears as a special, limiting case of the T-distributed random field model. The model is particularly useful whenever multiple, sparsely sampled realizations of the random field are available, and is clearly favourable to the Gaussian model in this case. The properties of the T-distributed random field model is demonstrated on well log observations from the Gullfaks field in the North Sea. The predictions correspond to traditional kriging predictions, while the associated prediction variances are more representative, as they are layer specific and include uncertainty caused by using variance estimates.