电阻率测井反演方法应用研究

Formation resistivity is one of the most important parameters to be evaluated in the evaluation of reservoir. In order to acquire the true value of virginal formation, various types of resistivity logging tools have been developed. However, with the increment of the proved reserves, the thickness of interest pay zone is becoming thinner and thinner, especially in the terrestrial deposit oilfield, so that electrical logging tools, limited by the contradictory requirements of resolution and investigation depth of this kinds of tools, can not provide the true value of the formation resistivity. Therefore, resitivity inversion techniques have been popular in the determination of true formation resistivity based on the improving logging data from new tools. In geophysical inverse problems, non-unique solution is inevitable due to the noisy data and deficient measurement information. I address this problem in my dissertation from three aspects, data acquisition, data processing/inversion and applications of the results/ uncertainty evaluation of the non-unique solution. Some other problems in the traditional inversion methods such as slowness speed of the convergence and the initial-correlation results. Firstly, I deal with the uncertainties in the data to be processed. The combination of micro-spherically focused log (MSFL) and dual laterolog(DLL) is the standard program to determine formation resistivity. During the inversion, the readings of MSFL are regarded as the resistivity of invasion zone of the formation after being corrected. However, the errors can be as large as 30 percent due to mud cake influence even if the rugose borehole effects on the readings of MSFL can be ignored. Furthermore, there still are argues about whether the two logs can be quantitatively used to determine formation resisitivities due to the different measurement principles. Thus, anew type of laterolog tool is designed theoretically. The new tool can provide three curves with different investigation depths and the nearly same resolution. The resolution is about 0.4meter. Secondly, because the popular iterative inversion method based on the least-square estimation can not solve problems more than two parameters simultaneously and the new laterolog logging tool is not applied to practice, my work is focused on two parameters inversion (radius of the invasion and the resistivty of virgin information ) of traditional dual laterolog logging data. An unequal weighted damp factors- revised method is developed to instead of the parameter-revised techniques used in the traditional inversion method. In this new method, the parameter is revised not only dependency on the damp its self but also dependency on the difference between the measurement data and the fitting data in different layers. At least 2 iterative numbers are reduced than the older method, the computation cost of inversion is reduced. The damp least-squares inversion method is the realization of Tikhonov's tradeoff theory on the smooth solution and stability of inversion process. This method is realized through linearity of non-linear inversion problem which must lead to the dependency of solution on the initial value of parameters. Thus, severe debates on efficiency of this kinds of methods are getting popular with the developments of non-linear processing methods. The artificial neural net method is proposed in this dissertation. The database of tool's response to formation parameters is built through the modeling of the laterolog tool and then is used to training the neural nets. A unit model is put forward to simplify the dada space and an additional physical limitation is applied to optimize the net after the cross-validation method is done. Results show that the neural net inversion method could replace the traditional inversion method in a single formation and can be used a method to determine the initial value of the traditional method. No matter what method is developed, the non-uniqueness and uncertainties of the solution could be inevitable. Thus, it is wise to evaluate the non-uniqueness and uncertainties of the solution in the application of inversion results. Bayes theorem provides a way to solve such problems. This method is illustrately discussed in a single formation and achieve plausible results. In the end, the traditional least squares inversion method is used to process raw logging data, the calculated oil saturation increased 20 percent than that not be proceed compared to core analysis.

A study of stone fragmentation in shock wave lithotripsy by customizing the acoustic field and waveform shape

Shock wave lithotripsy is the preferred treatment modality for kidney stones in the United States. Despite clinical use for over twenty-five years, the mechanisms of stone fragmentation are still under debate. A piezoelectric array was employed to examine the effect of waveform shape and pressure distribution on stone fragmentation in lithotripsy. The array consisted of 170 elements placed on the inner surface of a 15 cm-radius spherical cap. Each element was driven independently using a 170 individual pulsers, each capable of generating 1.2 kV. The acoustic field was characterized using a fiber optic probe hydrophone with a bandwidth of 30 MHz and a spatial resolution of 100 μm. When all elements were driven simultaneously, the focal waveform was a shock wave with peak pressures p+ =65±3MPa and p−=−16±2MPa and the −6 dB focal region was 13 mm long and 2 mm wide. The delay for each element was the only control parameter for customizing the acoustic field and waveform shape, which was done with the aim of investigating the hypothesized mechanisms of stone fragmentation such as spallation, shear, squeezing, and cavitation. The acoustic field customization was achieved by employing the angular spectrum approach for modeling the forward wave propagation and regression of least square errors to determine the optimal set of delays. Results from the acoustic field customization routine and its implications on stone fragmentation will be discussed.

GIS-based Association Between PM10 and Allergic Diseases in Seoul: Implications for Health and Environmental Policy.

PURPOSE: The role of PM10 in the development of allergic diseases remains controversial among epidemiological studies, partly due to the inability to control for spatial variations in large-scale risk factors. This study aims to investigate spatial correspondence between the level of PM10 and allergic diseases at the sub-district level in Seoul, Korea, in order to evaluate whether the impact of PM10 is observable and spatially varies across the subdistricts. METHODS: PM10 measurements at 25 monitoring stations in the city were interpolated to 424 sub-districts where annual inpatient and outpatient count data for 3 types of allergic diseases (atopic dermatitis, asthma, and allergic rhinitis) were collected. We estimated multiple ordinary least square regression models to examine the association of the PM10 level with each of the allergic diseases, controlling for various sub-district level covariates. Geographically weighted regression (GWR) models were conducted to evaluate how the impact of PM10 varies across the sub-districts. RESULTS: PM10 was found to be a significant predictor of atopic dermatitis patient count (P<0.01), with greater association when spatially interpolated at the sub-district level. No significant effect of PM10 was observed on allergic rhinitis and asthma when socioeconomic factors were controlled for. GWR models revealed spatial variation of PM10 effects on atopic dermatitis across the sub-districts in Seoul. The relationship of PM10 levels to atopic dermatitis patient counts is found to be significant only in the Gangbuk region (P<0.01), along with other covariates including average land value, poverty rate, level of education and apartment rate (P<0.01). CONCLUSIONS: Our findings imply that PM10 effects on allergic diseases might not be consistent throughout Seoul. GIS-based spatial modeling techniques could play a role in evaluating spatial variation of air pollution impacts on allergic diseases at the sub-district level, which could provide valuable guidelines for environmental and public health policymakers.

Prediction of adipose tissue composition using Raman spectroscopy: average properties and individual fatty acids.

Abstract: Raman spectroscopy has been used for the first time to predict the FA composition of unextracted adipose tissue of pork, beef, lamb, and chicken. It was found that the bulk unsaturation parameters could be predicted successfully [R-2 = 0.97, root mean square error of prediction (RMSEP) = 4.6% of 4 sigma], with cis unsaturation, which accounted for the majority of the unsaturation, giving similar correlations. The combined abundance of all measured PUFA (>= 2 double bonds per chain) was also well predicted with R-2 = 0.97 and RMSEP = 4.0% of 4 sigma. Trans unsaturation was not as well modeled (R-2 = 0.52, RMSEP = 18% of 4 sigma); this reduced prediction ability can be attributed to the low levels of trans FA found in adipose tissue (0.035 times the cis unsaturation level). For the individual FA, the average partial least squares (PLS) regression coefficient of the 18 most abundant FA (relative abundances ranging from 0.1 to 38.6% of the total FA content) was R-2 = 0.73; the average RMSEP = 11.9% of 4 sigma. Regression coefficients and prediction errors for the five most abundant FA were all better than the average value (in some cases as low as RMSEP = 4.7% of 4 sigma). Cross-correlation between the abundances of the minor FA and more abundant acids could be determined by principal component analysis methods, and the resulting groups of correlated compounds were also well-predicted using PLS. The accuracy of the prediction of individual FA was at least as good as other spectroscopic methods, and the extremely straightforward sampling method meant that very rapid analysis of samples at ambient temperature was easily achieved. This work shows that Raman profiling of hundreds of samples per day is easily achievable with an automated sampling system.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

Multiloop robust H∞ control design based on the dynamic PLS approach to chemical processes

In this paper, a multiloop robust control strategy is proposed based on H∞ control and a partial least squares (PLS) model (H∞_PLS) for multivariable chemical processes. It is developed especially for multivariable systems in ill-conditioned plants and non-square systems. The advantage of PLS is to extract the strongest relationship between the input and the output variables in the reduced space of the latent variable model rather than in the original space of the highly dimensional variables. Without conventional decouplers, the dynamic PLS framework automatically decomposes the MIMO process into multiple single-loop systems in the PLS subspace so that the controller design can be simplified. Since plant/model mismatch is almost inevitable in practical applications, to enhance the robustness of this control system, the controllers based on the H∞ mixed sensitivity problem are designed in the PLS latent subspace. The feasibility and the effectiveness of the proposed approach are illustrated by the simulation results of a distillation column and a mixing tank process. Comparisons between H∞_PLS control and conventional individual control (either H∞ control or PLS control only) are also made

Sistemas de controlo de gestão, empreendedorismo corporativo, estratégia e performance : contraste entre empresas familiares e não familiares

Mestrado em Contabilidade, Fiscalidade e Finanças Empresariais

Delineation of within-site terroir effects using soil and vine water measurement. Investigation of Cabernet Franc

. The influence of vine water status was studied in commercial vineyard blocks of Vilis vinifera L. cv. Cabernet Franc in Niagara Peninsula, Ontario from 2005 to 2007. Vine performance, fruit composition and vine size of non-irrigated grapevines were compared within ten vineyard blocks containing different soil and vine water status. Results showed that within each vineyard block water status zones could be identified on GIS-generated maps using leaf water potential and soil moisture measurements. Some yield and fruit composition variables correlated with the intensity of vine water status. Chemical and descriptive sensory analysis was performed on nine (2005) and eight (2006) pairs of experimental wines to illustrate differences between wines made from high and low water status winegrapes at each vineyard block. Twelve trained judges evaluated six aroma and flavor (red fruit, black cherry, black current, black pepper, bell pepper, and green bean), thr~e mouthfeel (astringency, bitterness and acidity) sensory attributes as well as color intensity. Each pair of high and low water status wine was compared using t-test. In 2005, low water status (L WS) wines from Buis, Harbour Estate, Henry of Pelham (HOP), and Vieni had higher color intensity; those form Chateau des Charmes (CDC) had high black cherry flavor; those at RiefEstates were high in red fruit flavor and at those from George site was high in red fruit aroma. In 2006, low water status (L WS) wines from George, Cave Spring and Morrison sites were high in color intensity. L WS wines from CDC, George and Morrison were more intense in black cherry aroma; LWS wines from Hernder site were high in red fruit aroma and flavor. No significant differences were found from one year to the next between the wines produced from the same vineyard, indicating that the attributes of these wines were maintained almost constant despite markedly different conditions in 2005 and 2006 vintages. Partial ii Least Square (PLS) analysis showed that leaf \}' was associated with red fruit aroma and flavor, berry and wine color intensity, total phenols, Brix and anthocyanins while soil moisture was explained with acidity, green bean aroma and flavor as well as bell pepper aroma and flavor. In another study chemical and descriptive sensory analysis was conducted on nine (2005) and eight (2006) medium water status (MWS) experimental wines to illustrate differences that might support the sub-appellation system in Niagara. The judges evaluated the same aroma, flavor, and mouthfeel sensory attributes as well as color intensity. Data were analyzed using analysis of variance (ANOVA), principal component analysis (PCA) and discriminate analysis (DA). ANOV A of sensory data showed regional differences for all sensory attributes. In 2005, wines from CDC, HOP, and Hemder sites showed highest. r ed fruit aroma and flavor. Lakeshore and Niagara River sites (Harbour, Reif, George, and Buis) wines showed higher bell pepper and green bean aroma and flavor due to proximity to the large bodies of water and less heat unit accumulation. In 2006, all sensory attributes except black pepper aroma were different. PCA revealed that wines from HOP and CDC sites were higher in red fruit, black currant and black cherry aroma and flavor as well as black pepper flavor, while wines from Hemder, Morrison and George sites were high in green bean aroma and flavor. ANOV A of chemical data in 2005 indicated that hue, color intensity, and titratable acidity (TA) were different across the sites, while in 2006, hue, color intensity and ethanol were different across the sites. These data indicate that there is the likelihood of substantial chemical and sensory differences between clusters of sub-appellations within the Niagara Peninsula iii

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Prediction of coagulation properties, titratable acidity and pH of bovine milk using mid-infrared spectroscopy

This study investigated the potential application of mid-infrared spectroscopy (MIR 4,000–900 cm−1) for the determination of milk coagulation properties (MCP), titratable acidity (TA), and pH in Brown Swiss milk samples (n = 1,064). Because MCP directly influence the efficiency of the cheese-making process, there is strong industrial interest in developing a rapid method for their assessment. Currently, the determination of MCP involves time-consuming laboratory-based measurements, and it is not feasible to carry out these measurements on the large numbers of milk samples associated with milk recording programs. Mid-infrared spectroscopy is an objective and nondestructive technique providing rapid real-time analysis of food compositional and quality parameters. Analysis of milk rennet coagulation time (RCT, min), curd firmness (a30, mm), TA (SH°/50 mL; SH° = Soxhlet-Henkel degree), and pH was carried out, and MIR data were recorded over the spectral range of 4,000 to 900 cm−1. Models were developed by partial least squares regression using untreated and pretreated spectra. The MCP, TA, and pH prediction models were improved by using the combined spectral ranges of 1,600 to 900 cm−1, 3,040 to 1,700 cm−1, and 4,000 to 3,470 cm−1. The root mean square errors of cross-validation for the developed models were 2.36 min (RCT, range 24.9 min), 6.86 mm (a30, range 58 mm), 0.25 SH°/50 mL (TA, range 3.58 SH°/50 mL), and 0.07 (pH, range 1.15). The most successfully predicted attributes were TA, RCT, and pH. The model for the prediction of TA provided approximate prediction (R2 = 0.66), whereas the predictive models developed for RCT and pH could discriminate between high and low values (R2 = 0.59 to 0.62). It was concluded that, although the models require further development to improve their accuracy before their application in industry, MIR spectroscopy has potential application for the assessment of RCT, TA, and pH during routine milk analysis in the dairy industry. The implementation of such models could be a means of improving MCP through phenotypic-based selection programs and to amend milk payment systems to incorporate MCP into their payment criteria.

Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy

The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict Miscanthus xgiganteus and short rotation coppice willow quality indices was examined. Moisture, calorific value, ash and carbon content were predicted with a root mean square error of cross validation of 0.90% (R2 = 0.99), 0.13 MJ/kg (R2 = 0.99), 0.42% (R2 = 0.58), and 0.57% (R2 = 0.88), respectively. The moisture and calorific value prediction models had excellent accuracy while the carbon and ash models were fair and poor, respectively. The results indicate that near infrared spectroscopy has the potential to predict quality indices of dedicated energy crops, however the models must be further validated on a wider range of samples prior to implementation. The utilization of such models would assist in the optimal use of the feedstock based on its biomass properties.

Evaluating mid-infrared spectroscopy as a new technique for predicting sensory texture attributes of processed cheese

The objective of this study was to investigate the potential application of mid-infrared spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates mid-infrared spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n = 32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4 degrees C, mid-infrared spectra ( 640 to 4,000 cm(-1)) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range ( 930 to 1,767 cm(-1)). The remaining attributes were most successfully modeled using a combined range ( 930 to 1,767 cm(-1) and 2,839 to 4,000 cm(-1)). The root mean square errors of cross-validation for the models were 7.4(firmness; range 65.3), 4.6 ( rubbery; range 41.7), 7.1 ( creamy; range 60.9), 5.1(chewy; range 43.3), 5.2(mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 ( melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions ( range error ratio = 9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that mid-infrared spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality..

Application of mid-infrared spectroscopy to the prediction of maturity and sensory texture attributes of cheddar cheese

The objective of this study was to determine the potential of mid-infrared spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n = 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The mid-infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had, corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that mid-infrared spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese..

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

Structural basis for selective inhibition of trypanosomatid glyceraldehyde-3-phosphate dehydrogenase: Molecular docking and 3D QSAR studies

The glycolytic enzyme glyceraldehyde-3 -phosphate dehydrogenase (GAPDH) is as an attractive target for the development of novel antitrypanosomatid agents. In the present work, comparative molecular field analysis and comparative molecular similarity index analysis were conducted on a large series of selective inhibitors of trypanosomatid GAPDH. Four statistically significant models were obtained (r(2) > 0.90 and q(2) > 0.70), indicating their predictive ability for untested compounds. The models were then used to predict the potency of an external test set, and the predicted values were in good agreement with the experimental results. Molecular modeling studies provided further insight into the structural basis for selective inhibition of trypanosomatid GAPDH.