Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

Prediction of rock mass P wave velocity using blasthole drilling information

In this paper, the development of the models for the prediction of rock mass P wave velocity is presented. For model development, the database of 53 cases including widely used and recorded drilling parameters and P wave velocity was constructed from the field studies conducted in 13 open pit lignite mines. Both conventional linear, non-linear multiple regression and Adaptive Neuro Fuzzy Inference System (ANFIS) were used for model development. Prediction performance indicators showed that ANFIS model presented the best performance and it can successfully be used for the preliminary prediction of P wave velocities of rock masses.     

Bioinformatic characterization of the extracellular lipases from Kluyveromyces marxianus

Lipases are hydrolytic enzymes that break the ester bonds of triglycerides, generating free fatty acids and glycerol. Extracellular lipase activity has been reported for the nonconventional yeast Kluyveromyces marxianus, grown in olive oil as a substrate, and the presence of at least eight putative lipases has been detected in its genome. However, to date, there is no experimental evidence on the physiological role of the putative lipases nor their structural and catalytic properties. In this study, a bioinformatic analysis of the genes of the putative lipases from K. marxianus L-2029 was performed, particularly identifying and characterizing the extracellular expected enzymes, due to their biotechnological relevance. The amino acid sequence of 10 putative lipases, obtained by in silico translation, ranged between 389 and 773 amino acids. Two of the analysed putative proteins showed a signal peptide, 25 and 33 amino acids long for KmYJR107Wp and KmLIP3p, and a molecular weight of 44.53 and 58.23 kDa, respectively. The amino acid alignment of KmLIP3p and KmYJR107Wp with the crystallized lipases from a patatin and the YlLip2 lipase from Yarrowia lipolytica, respectively, revealed the presence of the hydrolase characteristic motifs. From the 3D models of putative extracellular K. marxianus L-2029 lipases, the conserved pentapeptide of each was determined, being GTSMG for KmLIP3p and GHSLG for KmYJR107Wp; besides, the genes of these two enzymes (LIP3 and YJR107W) are apparently regulated by oleate response elements. The phylogenetic analysis of all K. marxianus lipases revealed evolutionary affinities with lipases from abH15.03, abH23.01, and abH23.02 families.     

随钻扩眼工具及技术研究

基于随钻扩眼技术优势的分析，研究了机械式、液压式和偏心式随钻扩眼工具执行机构和扩眼总成的机构原理，并介绍了国内所开展的相关研究。文章提出应加强现有工具的适应性和匹配性研究，发掘各类工具的优势。优化执行机构、扩眼总成和水力机构、重视底部钻具组合力学特性的分析是今后发展和完善随钻扩眼工具及技术的几个重要内容。实践表明，随钻扩眼技术在处理井下复杂情况、降低钻井综合成本、提高钻井速度、提高建井质量和安全性等方面具有显著的优势，随钻扩眼工具也正逐渐成为一种重要的石油钻井配套工具。     

钻井钢丝绳检测方法与装置的研究

钻井钢丝绳对于钻井平台安全可靠性有直接影响。采用磁性检测方法对钻井钢丝绳进行检测,并对钻井钢丝绳的励磁方式、聚磁方法及传感器与钢丝绳的配合间隙问题进行分析。在此基础上,研制了钻井钢丝绳检测仪器,能够实现对钻井钢丝绳断丝、磨损等缺陷的定性、定量检测,并能够对缺陷进行定位,为钻井钢丝绳的寿命评估提供了依据,对提高钻井平台的安全性具有重要意义。     

PDC钻头水力结构优化设计研究

在PDC钻头工作过程中，钻井液对钻头体表面的冲洗、冷却和润滑是保证钻头正常工作的一个非常重要的条件。对PDC钻头而言，水力结构（主要是中心水眼和冠部水道）设计的重要性尤其突出。以前,对钻头水力系统研究只能通过实验的方法进行,研究周期长、成本高、结构调整不方便，而数值模拟的方法在几年前还不成熟，甚至静态的复杂结构流场问题基本无法解决。为此，在提出PDC钻头水力结构优化设计原则的基础上，对PDC钻头的三维流场进行了数值模拟。模拟中考虑了钻头的喷嘴布置位置、直径、数量以及切削齿对流场的影响。计算结果表明，原设计在喷嘴布置位置和喷射角度上存在不足，以此为基础进行了水力结构的优化设计。文中的研究成果成功地应用在新型钻头水力结构的设计中，研究方法为PDC钻头水力结构优化分析奠定了理论基础。     

泥页岩井壁稳定研究及在临盘地区的应用

从岩石力学和物理化学两个主要因素简要分析了井壁失稳的机理，指出泥页岩井壁失稳是由力学与化学两方面因素共同作用的结果。钻井液与泥页岩存在化学势差，并改变了井壁附近的孔隙压力，降低岩石强度。借助于井壁处有效应力的变化,将泥页岩与钻井液相互作用时页岩水化所产生的力学效应与纯力学效应结合起来，计算出任意井斜方位井眼围岩应力状态，利用测井资料求出有关岩石力学参数和Mohr Couloumb准则，计算得出防塌的临界钻井液密度。     

TZJ40/2250拖挂钻机的研制

根据油田对钻井工程减少作业时间、提高移运速度和降低成本的需要,南阳二机石油装备(集团)有限公司综合了移动式钻机和橇装模块化钻机的结构特点和设计思想,以安全为主,高效和环保为补充,研制开发了TZJ40/2250拖挂钻机。钻机采用了半挂车行车制动、半挂车转向控制、液压盘式刹车和液力传动箱等关键技术,大大提高了钻机的自动化程度。出口和现场应用表明,该型钻机不仅具有结构简单、安全可靠和设计紧凑等优点,而且具有移运方便和使用范围广等不可比拟的优势。     

机械-射流破岩耦合特性研究

在现代旋转钻井破碎井底岩石中仍是以机械破岩为主，理论与实践证明，射流辅助破岩是提高钻井破岩效率的重要途径，但它们的耦合特性研究极少。文章基于渗流场与应力场的耦合理论分析，进行了机械与射流破岩耦合特性的实验研究。结果表明：射流压力和水楔作用对岩石渗流场、应力场具有重要作用，耦合作用比非耦合作用的破岩效率有较大幅度的提高。在实验条件下，砂岩的耦合作用提高破岩效率40％左右，灰岩的耦合作用提高破岩效率20％左右。