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K reservoir is an important oil and gas producing layer of H oilfield in the Middle East, Iraq. The reservoir space is mainly matrix pores and dissolved pores, with a wide range of permeability and poor correlation between porosity and permeability. The reservoir thickness is large, and the stratigraphic heterogeneity is very strong; therefore, it is poor quality to identify carbonate reservoir fluid properties only relying on conventional logging data and traditional logging evaluation methods. Aiming at this problem, the fluid identification work of K reservoirin H oilfield was carried out. By analyzing conventional logging data, it is found that the ratio of deep to shallow resistivity can better distinguish water from hydrocarbon. Based on analyzing morphological characteristics of the total hydrocarbon curve and corresponding reservoir fluid properties, it is found that the gas curve has obvious differences in the morphology of different fluid properties, so it is considered to further classify water and hydrocarbon by using gas curve. It is found that the ratio of heavy hydrocarbon tohydrocarbon gas density index can better classify oil-water layer and water layer. In order to quantitatively characterize the identification process, the identification method of water-oil-water layer gas measurement curve (ECR1) is established. ECR1 greater than 0 is oil-water layer, otherwise, it is water layer. Based on gas wet index, light hydrocarbon ratio and excavation effect, the identification method of gas-reservoir gas measurement curve (ECR2) is established. ECR2 higher than 0 isgas reservoir, and vice versa. The application of this model to 38 small layers in 13 wells of K reservoir in H oilfield shows that the recognition coincidence rate reaches 81.58%, and the recognition accuracy is high, meeting the actual needs of study area. The established ECR model has achieved good application effect in K reservoir of H oilfield, which can provide a certain reference for the subsequent exploration and development of this area, and also provide a reference for fluid identification of similar carbonate reservoirs worldwide.

The traditional gas logging using hydrogen flame gas chromatography is gradually difficult to meet the requirements of the current complex oil and gas reservoir exploration because of its long detection cycle, many additional equipment, complex use, maintenance process and other defects. Infrared spectroscopy has obvious advantages in detection speed and convenience. But when it comes to homologues with similar molecular structure, it is difficult to quantitatively analyze the component concentration because of the serious overlap of characteristic absorption peaks. To solve this problem, this paper uses the self-developed experimental platform to carry out a large number of infrared spectral experimental studies on the single substance and six component mixtures. Based on a large number of experimental data, a method combining the AdaBoost classifier with optimization window partial least squares (PLS) modeling is proposed. Through the PLS local modeling for the spectra of different components and different concentration ranges, the boosting algorithm is used to classify the original spectral data, and then the component concentration is calculated. This method solves the problems of difficult identification and inaccurate quantitative analysis of alkane mixed gas components in traditional methods, and effectively improves the detection accuracy of infrared spectrum quantitative analysis of mixed components, which provides the core theoretical support for infrared spectroscopy to replace hydrogen flame gas chromatography which is widely used in gas logging. [ABSTRACT FROM AUTHOR]

[Display omitted] • Methanol is prevalent in offshore logging gases but not onshore fields. • Methanol concentrations in offshore logging gases may exceed 1 vol%. • Methanol interferes with the analysis of other logging gas components. • There is no strong correlation between methanol and other logging gas components. Methanol infrared spectral characteristics in offshore gas logging. Methanol was found in the logging gases during offshore gas logging by infrared spectra. Logging gas spectra from over 300 offshore wells and 1,000 onshore wells were processed and analyzed, and methanol characteristics were prevalent in the offshore logging gases but not onshore logging gases. The spectra of instruments Alpha111829 and Alpha111834 are studied as examples. The methanol concentrations in some areas exceeded 1 vol%, seriously interfering with the other measurements of alkane gases. Moreover, there are rapid changes in the trends of methanol concentrations. It indicates that methanol does not originate from drilling fluid additives. The results of the correlation analysis show that there are no strong correlations between methanol and the other logging gas component concentrations. Therefore, methanol may be neither a judgmental indicator component gas for natural gas nor oil but a separate energy source. We believe methanol in the offshore logging gases originates from strata. The discovery and analysis of methanol in offshore logging gases are significant in atmospheric and marine environments, oil and gas exploration, geochemistry, and energy. [ABSTRACT FROM AUTHOR]

The article is devoted to the development of new methodology of gas-logging interpretation based on materials from drilled wells of the Pavlovsky oil field in the Lower-Middle-Visayan terrigenous oil and gas complex in the interval of the Tula terrigenous horizon С1tl, with using elements of mathematical statistics - stepwise discriminant analysis, for which the gas chromatographic data (the total gas content of hydrocarbons in the drill mud and component composition of the gas-air mixture) as well as data of luminescent-bitumen analysis of drill cuttings were used as variables. In addition, logging data were used - gamma-ray logging, neutron-neutron logging for thermal neutrons and the difference between the bit diameter and the well diameter. Based on the results of the analysis, the probability of attributing observations to oil-saturated sandstones, which takes into account gas logging and well logging, was calculated. In addition to oil-saturated sandstones, rocks occurring in this oil-gas-bearing complex were studied: mudstones, aleurolites, clayey sandstones and non-oil saturated sandstones. To visualize the results, a geological and geophysical diagram was plotted on which were shown: rock saturation according to well survey, rock saturation according to well logging and lithological column according to well survey and logging, as well as all variables that participated in the discriminant analysis. Based on the discriminatory analysis, three ranges of values were identified: 1) an area with observations related to oil-saturated rocks; 2) an area with observations related to non-oil-saturated rocks and 3) an area with observations of unclear saturation with indicators that are intermediate (the transition zone is possibly oil-saturated or washed reservoirs).

Infrared spectroscopy (IR) quantitative analysis technology has shown excellent development potential in the field of oil and gas logging. However, due to the high overlap of the IR absorption peaks of alkane molecules and the offset of the absorption peaks in complex environments, the quantitative analysis of IR spectroscopy applied in the field puts forward higher requirements for modelling speed and accuracy. In this paper, a new type of fast IR spectroscopy quantitative analysis method based on adaptive step-sliding partial least squares (ASS-PLS) is designed. A sliding step control function is designed to change the position of the local PLS analysis model in the full spectrum band adaptively based on the relative change of the current root mean square error and the global minimum root-mean-square error for rapid modelling. The study in this paper reveals the influence of the position and width of the local modelling window on the performance, and how to quickly determine the optimal modelling window in an uncertain sample environment. The performance of the proposed algorithm has been compared with three typical quantitative analysis methods by experiments on an IR spectrum dataset of 400 alkane samples. The results show that this method has a fast quantitative modelling speed with high analysis accuracy and stability. It has important practical value for promoting IR spectroscopy gas-logging technology.

This paper mainly introduced the design principle and user interface implementation as well as optimization of quantitative analysis method of software system for domestic first gas measuring instrument which is based on the fast gas chromatography quadrupole mass spectrometer. The WGA1000 software system is a real-time acquisition and control system. It's based on Windows operating system, MFC programming realization, and its interface style is similar to the Office. The theme framework includes chromatogram control, mass spectrometer control, data acquisition, data playback, component quantitation, alarm and record as well as temperature and pressure control, ion scanning method, peak judgment, peak intensity calculation and concentration correction. So far, the software system has realized that instrument running state and parameters were precisely controlled, the real-time concentration values of each component were obtained by collecting and processing the data quickly, it's worth noting that the concentration values were received through the software automatically deducting the background density under the condition of the same depth, the accuracy of the results are higher. Then the results were transmitted to the terminal service system, and finally the variation tendency of each component concentration was obtained in the drilling process. By observing and analyzing the change trend of each component concentration, the staff can quickly judge underground strata structure and the content of oil and gas. This work can largely improve the work efficiency and provide a guarantee for drilling safety. We found that in the previous work conventional linear fitting method tends to cause the intercept of fitting equation too big, leading to low signal concentration calculation error, It's unsuitable for larger linear range data analysis, moreover, conventional ion scanning period is longer and leads to the less number of the points above the peak and the poor peak shape stability. Adopting subsection calibration can avoid the calculation error of the highest point and the lowest point and segmented ion scanning can increase the number of the points above the peak, quantitative results and repeatability of the target components have been significantly improved by optimizing the linear correction method and ion scanning mode. Another major breakthrough about the system is automatic step calculation especially for methane in order to increase the dynamic range. It has been approved after long-term field application and simulation experiments, WGA1000 software system combined with hardware and data analysis software can meet the real-time monitoring requirement of on-line gas measuring instrument in gas logging system. [ABSTRACT FROM AUTHOR]

As one of the main means of discovering and evaluating oil and gas reservoirs, gas logging technology plays an irreplaceable role in oil and gas reservoir exploration. However, with the increasing complexity of oil and gas reservoir exploration, gas logging technology has gradually shifted from chromatographic gas logging to infrared spectral gas logging. Infrared spectroscopy logging technology can not only quickly analyze the content of hydrocarbon components in formation fluids, but also has the advantages of no pollution, high detection efficiency, and accurate analysis. However, there are many types of hydrocarbon gases in formation fluids and a wide range of concentration ranges. How to ensure that infrared spectroscopy can accurately measure the content of hydrocarbon gases is an urgent problem to be solved. To solve this problem, this paper proposes the beetle antennae search algorithm (BAS) combined with the competitive adaptive reweighted sampling (CARS) to improve the SVR for modeling. Firstly, the characteristic wavelength points of the sample set are obtained by CARS, and then the parameters of SVR are optimized by BAS, and then the corresponding high, medium and low concentration prediction models are established. The experimental results show that the accuracy and efficiency of the proposed CARS-BAS-SVR method in the quantitative analysis of hydrocarbon gas components are better than those of the traditional method. Its prediction accuracy for low-concentration hydrocarbon gases is greatly improved, and the overall average error is less than 1‰. It effectively improves the detection accuracy of infrared spectroscopy logging technology, and provides support for the further promotion of infrared spectroscopy in gas logging. • Quantitative analysis of spectra with a large-span. • Quantitative spectral analysis at low concentrations. • Faster wavelength selection method. • A new measurement method for gas logging. [ABSTRACT FROM AUTHOR]

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The recent introduction of infrared spectroscopy gas logging technology is of great significance for the timely discovery of oil and gas, the prediction of well kicks, the prevention of blowout and gas invasion, and the comprehensive evaluation of reservoirs. However, the accuracy and stability of the current quantitative analysis methods for gas logging measurement data are limited by the severe overlap of infrared absorption spectra and the difficulty of sample preparation. In this paper, a novel hybrid method based on particle swarm optimization split peak fitting and a support vector machine (PSS) is presented to address this issue. The method uses a single peak that satisfies a specific absorption line to fit the mixed spectrum and classifies and identifies the parameters of a fitted single peak to complete the "mathematical separation" of the overlapping spectrum. The IR spectral data of 761 samples for methane, ethane, propane, n-butane, iso-butene, n-pentane, iso-pentane and carbon dioxide were collected for quantitative analysis experiments. Repeated comparison experiments of different feature extraction methods under the same partial least squares (PLS) regression method show that the PSS model established with fewer samples still has better analytical accuracy and stronger robustness. At the same time, PSS can significantly improve the model accuracy under different regression methods to a similar level, reducing the dependence on regression methods. PSS combined with PLS can simultaneously measure the contents of eight components in a multicomponent mixture, and the predicted average root mean square error is 0.7242% for 60 mixed experimental gas samples and 0.2534% for 3 industrial standard gas samples. The proposed method has a high practical application value for popularizing IR spectroscopy gas logging technology and provides a new concept for the quantitative detection of substances with high molecular similarity. • A new IR gas logging technology with excellent performance and low cost was realized. • PSS method for separating infrared overlapping peaks of hydrocarbons is proposed. • Rapid detection of eight substances, including C 1 ∼ C 5 and CO 2 , based on PLS. • The method improves the accuracy of regression models with stronger compatibility. [ABSTRACT FROM AUTHOR]

Infrared spectroscopy (IR) quantitative analysis technology has shown excellent development potential in the field of oil and gas logging. However, due to the high overlap of the IR absorption peaks of alkane molecules and the offset of the absorption peaks in complex environments, the quantitative analysis of IR spectroscopy applied in the field puts forward higher requirements for modelling speed and accuracy. In this paper, a new type of fast IR spectroscopy quantitative analysis method based on adaptive step-sliding partial least squares (ASS-PLS) is designed. A sliding step control function is designed to change the position of the local PLS analysis model in the full spectrum band adaptively based on the relative change of the current root mean square error and the global minimum root-mean-square error for rapid modelling. The study in this paper reveals the influence of the position and width of the local modelling window on the performance, and how to quickly determine the optimal modelling window in an uncertain sample environment. The performance of the proposed algorithm has been compared with three typical quantitative analysis methods by experiments on an IR spectrum dataset of 400 alkane samples. The results show that this method has a fast quantitative modelling speed with high analysis accuracy and stability. It has important practical value for promoting IR spectroscopy gas-logging technology. [ABSTRACT FROM AUTHOR]

• Quantitative analysis of spectra with a large-span. • Quantitative analysis method of mixed component spectroscopy. • Selection of calibration subset for quantitative analysis of large-span Spectroscopy. • A new measurement method for gas logging. As an important part of integrated logging, gas logging can directly measure the content and composition of hydrocarbon gas in the formation, and plays an irreplaceable role in the discovery and evaluation of oil and gas while drilling and real-time drilling parameter monitoring. Traditional gas logging uses chromatographs to analyze downhole gas. However, due to its long measurement period, many additional instruments, long lines, strict environmental requirements, complex operations, and easy distortion, it has been difficult to meet the requirements of current exploration work in complex oil and gas reservoirs. With the advantages of fast analysis speed, rich detection parameters, short delay time, good stability, and strong anti-pollution ability, the quantitative infrared spectroscopy technique can replace chromatography for hydrocarbon gas measurement. However, when it is used for the quantitative analysis of downhole hydrocarbon gases, a large span of absorption spectra and overlapping characteristic peaks can occur due to the complexity of gas components, making quantitative identification more difficult. To address the above problems, this paper proposes a method of net signal analysis (NAS) combined with density peak clustering (DPCA) for local PLS modeling. The method first obtains the net signals of the unknown and correction samples by the net signal analysis and then clusters the net signals by DPCA to select the corresponding local correction for PLS modeling prediction. The experimental results show that the NAS-DPCA-PLS method proposed in this paper is significantly better than the traditional method in the quantitative analysis of hydrocarbon mixed gas components, and the average accuracy of mixed component prediction reaches more than 98%, which effectively improves the detection accuracy of quantitative analysis of mixed component infrared spectroscopy and provides important theoretical support for the use of infrared spectroscopy in gas logging. [ABSTRACT FROM AUTHOR]