Oil Test Results Research Articles

Intelligent Identification and Quantitative Evaluation Method of Fluid Components from T2-T1 2D NMR Logging

Summary T2-T1 2D nuclear magnetic resonance (NMR) logging plays an important role in fluid identification and reservoir evaluation. When the signal/noise ratio (SNR) is low, the response of the fluid component overlaps on the NMR 2D spectrum, and the fluid characteristic map obtained in the laboratory could not be directly applied to 2D NMR logging data. In this work, we proposed a T2-T1 2D NMR fluid identification and quantitative evaluation method based on blind source separation (BSS) and hierarchical clustering. This method fixes the number of fluid components of BSS as eight and utilizes a hierarchical clustering method to further process the 2D spectra obtained from BSS, so that each spectrum contains only one fluid component response. Subsequently, the fluid types are determined according to the response characteristics, and the fluid volumes are quantitatively calculated. Based on the synthetic data of T2-T1 2D NMR logging, this method was used to calculate the volumes of the fluid components, and the accuracy of the method was verified by comparing with the model and other four methods. This method was applied to 2D NMR logging data from Well Z and Well G and verified by oil test results. The results show that the relative error between the fluid volumes calculated by the proposed method and the model is smaller than other methods. The data processing results of Well Z and Well G are consistent with those of the oil test. Both synthetic data and actual logging data show that this method can provide the accurate fluid identification and quantitative evaluation results of T2-T1 2D NMR logging data.

Experience in Processing Alternative Crude Oils to Replace Design Oil in the Refinery

A comprehensive investigation of a highly complex petroleum refinery (Nelson complexity index of 10.7) during the processing of 11 crude oils and an imported atmospheric residue replacing the design Urals crude oil was performed. Various laboratory oil tests were carried out to characterize both crude oils, and their fractions. The results of oil laboratory assays along with intercriteria and regression analyses were employed to find quantitative relations between crude oil mixture quality and refining unit performance. It was found that the acidity of petroleum cannot be judged by its total acid number, and acid crudes with lower than 0.5 mg KOH/g and low sulphur content required repeated caustic treatment enhancement and provoked increased corrosion rate and sodium contamination of the hydrocracking catalyst. Increased fouling in the H-Oil hydrocracker was observed during the transfer of design Urals crude oil to other petroleum crudes. The vacuum residues with higher sulphur, lower nitrogen contents, and a lower colloidal instability index provide a higher conversion rate and lower fouling rate in the H-Oil unit. The regression equations developed in this work allow quantitative assessment of the performance of crucial refining units like the H-Oil, fluid catalytic cracker, naphtha reformer, and gas oil hydrotreatment based on laboratory oil test results.

Evaluation of the Geological Characteristics and Exploration Potential of Tight Oil in the Neogene Upper Ganchaigou Formation in the Zhahaquan Area, Qaidam Basin.

The study of the geochemical characteristics of source rocks is an important part of tight oil evaluation. The Zhahaquan area of the Qaidam Basin is a new area for tight oil exploration in China. During the sedimentary period of the Neogene Upper Ganchaigou Formation (N1) in the Zhahaquan area, a set of source rocks of semideep lake and deep lacustrine facies as well as a set of thin, interbedded fine sandstone and argillaceous limestone was deposited, providing favorable conditions for the formation of tight oil. However, the study on the geochemical characteristics of source rocks in this area is relatively weak. The geochemical characteristics of the source rocks in the Zhahaquan area were determined via the experimental analysis of parameters such as vitrinite reflectance (Ro), chloroform bitumen "A", total organic carbon (TOC), group components, kerogen types, rock pyrolysis, and aromatic compounds of crude oil. The following results were obtained: the Zhahaquan area had II1-type hydrocarbon source rock organic matter, and the TOC was 0.32%-1.32%. The type index (TI) was 48.70-72.23, the chloroform bitumen "A" content was 0.0034%-0.1133%, Ro was 0.810%-1.265%, and the cracking hydrocarbon peak temperature (Tmax) was distributed in the temperature range of 362-444 °C. The hydrocarbon generation conversion rate was 0.89%-10.0%. The reservoir mainly had intergranular pores, dissolution pores, and microfractures. The average porosity was 8.0%, and the average permeability was 0.861 × 10-3 μm2. The average oil saturation was 30.0%, and the average water saturation was 21.6%. From a comprehensive analysis of the results, the following inferences were derived. The parent material of the source rocks in the Zhahaquan area mainly originated from algae and other phytoplankton in the lake basin, which was a good source rock for oil exploration. The source rocks of this area have entered the threshold of hydrocarbon generation and are in the peak oil-generation stage. They have potential as industrial oil and gas resources. The oil test results of YD103 and seven other wells showed that the daily oil index per meter ranges from 0.38 to 6.5 m3/d·m, indicating that the source rocks have the ability to form industrial oil. Analysis of the geochemical characteristics of source rocks and study of reservoir geological characteristics will provide theoretical support and reference for tight oil exploration and development in Zhahaquan.

Novel fluid typing method of NMR dual-TW logging in mixed-wet reservoirs

In mixed wetting reservoirs, the fluids typing is difficult by using nuclear magnetic resonance (NMR) logging. The data processing and interpretation method of NMR dual-waiting time (TW) activation faces serious challenges and the previous NMR time-domain analysis (TDA) of NMR dual-TW logging for water-wet condition is not applicable. The petrophysical characteristics under mixed-wet conditions was studied and the oil-wet factor was defined, and new NMR logging response equations was investigated and the echo train difference between long and short wait times were derived. The relaxation parameters of fluids and water saturation was finally calculated by the genetic algorithm. Therefore, a new improved TDA method was proposed for mixed-wet reservoirs. To verify the method, a petrophysical model was set and some simulations proved it accurate. A case study from Chang 8, Ordos Basin, China, was studied, and the oil saturation and volume in the flushed zone were calculated. From the oil-testing results, two cross-plots for fluid typing were constructed based on the calculated oil saturation in flushed zone, T2 geomean difference of dual-TW NMR logging. The practical application shows that the novel TDA method of NMR dual-TW logging for mixed-wet reservoirs is efficient for the fluid identification.

Gas Turbine Bearing Temperature Monitoring via Regression Modelling

This paper focuses on using Regression technique (MLR) towards finding solution to incidence of high compressor bearing temperature on one of the units at Geregu power plant in Ajaokuta, Nigeria. Monitoring of parameters related to the bearing temperature was carried out to find out causes for the high bearing temperature fault and came up with successful diagnosis by interrelating the gas turbine current lube oil test results of parameters like the kinematic viscosities, % concentration of additives and flash point with reference and standard VG46 lube oil data published in literature. Using statistical tools like the Pearson correlation and co-variant metrics for the five-years, the oil viscosities at 100oC and 40oC were selected as the input of the MLR model based on their Pearson coefficients of (-98.08%) and (-99.68%) respectively relative to the compressor bearing temperature. The MLR model for the bearing temperature prediction gave a root mean square error of 0.121 and coefficient of determination (R2) of 99.71%. The model predicts that by the 2nd quarter of 2025, the bearing temperature would have reached the alarm point (900C) from the current value of 850C and that by the 1st quarter of 2027, the bearing temperature would have reached the trip point (1200C). Conclusion reached is that a well formulated data driven model can reliably forecast bearing temperature and together with sensors aid in gas turbine condition monitoring. Likewise, it is concluded that shearing due to the consistent high temperature operation of the gas turbine lube oil is responsible for the depletion of the Zinc (-23.9%) and Magnesium(-26%) additives leading to the decay in the viscosity and consequent bearing temperature increment. Recommendation made is to either replenish oil with anti-wear additives or completely replace the oil to minimize the bearing wear rate and thus the bearing temperature.

An advanced prediction model of shale oil production profile based on source-reservoir assemblages and artificial neural networks

Over the past decade, hydrocarbon production from shale oil reservoirs has become increasingly common, and successful shale oil exploration and development depends significantly on the accurate evaluation of the sweet spots. However, different scholars have established different evaluation standards for sweet spots under different geological settings, and it is difficult for these standards to form a universal evaluation standard. The sweet spots should be synonymous with the overall combination of geological, engineering and economic sweet spots. The shale oil production evaluation would be a valid indicator due to the comprehensive combination of the above three perspectives. This paper demonstrates a multidisciplinary data-driven workflow to predict shale oil production through machine learning and quantitative evaluation of geological variables. 48 test sections from 30 exploratory wells in the Lucaogou Formation of the Jimusaer Sag are taken as an example for application demonstration. The proposed 13 geological variables based on source-reservoir assemblage types, source rock quality, reservoir quality, migration dynamics, and conduit conditions realize a systematic and comprehensive characterization of hydrocarbon generation, storage, dynamics, and flow stimulation. Based on the different averaging algorithms in the ANN model, good agreement has been observed between predicted and simulated data for training (R > 0.95) and validation (R > 0.87). Moreover, the geometric and harmonic averaging algorithms are preferentially recommended to characterize reservoir heterogeneity. In unconventional reservoirs, there is no single attribute that can be used to predict success or failure. The training results of the advanced prediction model are better than the other five single reservoir characterization models. On the well J174 dataset, the sweet spot predicted by the model matches well with the oil test results. The increase in liquid hydrocarbon content, mud gas content, TOC and normal faults percentage has positive effects on shale oil production, while the increase in reverse faults percentage has negative effects on shale oil production. This research provides ideas for intelligent prediction of the distribution of sweet spots in unconventional resources, and is also important for the development of intelligent hydrocarbon exploration technology.

Preparation and Functional Evaluation of Compound Bletilla striata Essence

The purpose of this study was to prepare a multi-functional skin care product and apply more plant resources to the skin care industry. The crude polysaccharides from Bletilla striata, Panax ginseng, and Tremella fuciformis were extracted under different conditions based on the characteristics of the plants. The formula ratio of the compound Bletilla striata essence was optimized through an orthogonal experiment. The moisturizing, whitening, and antioxidant properties of the compound essence were evaluated by the weighing method, tyrosinase activity test, and DPPH free radical scavenging test, respectively. Finally, the improvement degree of skin condition was detected using a skin water and oil test instrument. The results showed that the polysaccharide contents of Bletilla striata, Panax ginseng, and Tremella fuciformis were 18.00±0.5, 41.70±0.7, and 20.25±0.6%, respectively. The optimal formula for the essence was: Bletilla striata extract 12 mg, Panax ginseng extract 8 mg, Tremella fuciformis extract 8 g, sodium hyaluronate 20 mg, and ultra-pure water 50 mL. The moisture retention rate of the essence was 80.00±3.0%, the tyrosinase inhibition rate was 69.04±2.2%, and the DPPH radical scavenging rate was 71.03±2.5%. The skin water and oil test results showed that the compound essence can increase skin moisture and reduce skin oil. The product can reduce the content of pores, stains, wrinkles, roughness, acne and pigmentation, and has the potential to become a functional, natural and safe skin care product.

Nuclear magnetic resonance pore radius transformation method and fluid mobility characterization of shale oil reservoirs

The mobility of fluid is important for evaluating shale oil reservoirs, and it is closely related to the pore structure. Nuclear magnetic resonance (NMR) can effectively reveal the pore size distribution from nanometer to micron, providing a way to study the relationship between pore structure and mobility. However, the transverse relaxation time (T2) of NMR also contains bulk relaxation and diffusion relaxation, which will affect the accuracy of pore structure calculation. To characterize the mobility of shale oil by NMR, we proposed a pore radius transformation method with relaxation correction. The NMR simulations were carried out based on different digital core models, which were constructed according to X-CT results of shale cores. And the comparisons were performed to verified the accuracy and efficiency of the presented method. Then, the relation of geometric means of pore radius and mobility was studied through NMR experiments, and the NMR logging data of well J31 were processed by proposed method. The results show that the relative errors between corrected T2 distributions and surface relaxation distributions are smaller, and the geometric means of pore radius after relaxation correction are closer to those of digital core models, which proves the efficiency of the presented method. The logarithm of geometric mean of pore radius obtained from T2 distribution of shale core NMR experiment is positively correlated with S1/TOC, which can represent shale mobility. The processing results of NMR logging data in well J31 show two sections with large geometric means of pore radius, indicating that they have good mobility, which are consistent with the oil test results.

Optimization of Logging Interpretation Parameters Based on Committee Machine

Aiming at the problems of low precision and low applicability of the selection method of macroscopic capture cross-section parameters of rock skeleton, formation water, oil and gas, and mud in the volume model of remaining oil saturation logging, this study proposes a method for optimizing the capture section parameters based on the committee machine regression model. To begin, we select well sections from well logging data in a reasonable manner, allocate well sections with different component parameters in the same proportion, and construct sample datasets. The basic experts are then selected as multiple regression models, particle swarm optimization, and robust regression methods to train and learn the input parameters. By combining multiple experts, the regression committee machine improves the overall performance of the intelligent model. Finally, the genetic algorithm is used as a combiner to determine the contribution of each basic expert network in the final output, and the optimized parameters are obtained, which are then fed into the volume model to calculate the remaining oil saturation of the newly developed production wells, guiding the perforation and development work. The model is used to evaluate the remaining oil in the X oilfield, and the calculated water saturation matches the oil test results, proving the model’s accuracy and availability. The use of real-world data demonstrates that this method can effectively characterize the four parameter values in the volume model and provide reliable geophysical technical support for the evaluation of remaining oil.

Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China

The Triassic Yanchang Formation in the Pengyang area of Ordos Tianhuan depression is an important oil and gas formation. However, most of the oil pays in the study formations are low-resistivity or low-contrast reservoirs with low permeability, bringing challenges to the reservoir identification and evaluation by well logs. In this paper, we first measured the nuclear magnetic resonance (NMR), phase permeability, cation exchange capacity (CEC), and X-ray diffraction for core samples. Then, the genetic types for the low-resistivity pays were analyzed based on the experiment results, water analysis, and well log data collected. It was found that large variations of formation water salinity, high irreducible water saturation, and clay conductivity are the primary genetic types. Further, the random forest (RF) algorithm with sensitive parameter inputs was used to identify the oil, oil and water, and water layers. The anomaly of spontaneous potential (∆SP) that characterizes water salinity, the relative value of gamma ray log (∆GR) that describes the bound water content, resistivity, density, and acoustic logs were taken as sensitive logs according to the genetic analysis. Finally, this identification method was verified by comparison with the traditional crossplot method and oil test results. The identification accuracy of the RF is 90%, far higher than that by the crossplot method.

Correction Method for Logging Curves in Clay-Rich Tight Glutenite Reservoir: Upper Wuerhe Formation in Mahu Oilfield, China

Mahu Oilfield is the largest tight glutenite oilfield in the world, and the upper Wuerhe formation is an important succeeding exploration horizon. However, the upper Wuerhe formation in the Mahu 1 zone has a high clay content, which can lead to serious wellbore collapse. Meanwhile, the horizontal well logging is not corrected. These factors lead to the inconsistency between the logging interpretation results and the oil test results. The interpretation precision of the clay content, water saturation, and porosity, which are crucial to reservoir evaluation, is very low. In this paper, a workflow of logging curve correction using multiple methods is proposed. The multiple linear fitting is used to correct boreholes, and then histogram frequency distribution matching is used to standardize multi-well logging curves. Finally, the optimization method is used to build a volume model based on skeleton analysis, and the results are calibrated with core analysis results. Horizontal well density logs are corrected using adjacent vertical well logs. The interpretation results of clay content, water saturation, and porosity with high precision are obtained. The reservoir interpretation is more in line with the oil test results than the original interpretation. The clay content distribution is more reasonable.

Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data

In the early exploration of many oilfields, low-resistivity-low-contrast (LRLC) pay zones are easily overlooked due to the resistivity similarity to the water zones. Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated. In this study, after analyzing a large number of core samples, main causes of LRLC pay zones in the study area are discerned, which include complex distribution of formation water salinity, high irreducible water saturation due to micropores, and high shale volume. Moreover, different oil testing layers may have different causes of LRLC pay zones. As a result, in addition to the well log data of oil testing layers, well log data of adjacent shale layers are also added to the original dataset as reference data. The density-based spatial clustering algorithm with noise (DBSCAN) is used to cluster the original dataset into 49 clusters. A new dataset is ultimately projected into a feature space with 49 dimensions. The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron (MLP). A total of 3192 samples are used for stratified 8-fold cross-validation, and the accuracy of the MLP is found to be 85.53%.

Microscopic response mechanism of electrical properties and saturation model establishment in fractured carbonate rocks

Fractures are common in carbonate formations, and it is challenging to characterize electrical properties and establish saturation models for the fractured rocks by conventional petrophysical experiments. Therefore, it is necessary to study the fractures' electrical conduction and establish water saturation models by pore-scale numerical simulation and microscopic analyses. First, a multi-composition digital rock is constructed by the X-ray CT scan, Maps, and quantitative evaluation of minerals by scanning electron microscopy multi-scale supporting experiments. Next, the fractal Brown motion is used to construct the fractured digital rocks, and the lattice Boltzmann method is used to simulate the oil-water distribution. Then, the resistivities of these digital rocks are calculated by the finite element method. Finally, the microscopic response mechanism of the electrical properties is studied in detail, and new water saturation models are established. The study shows that resistivity decreases as the fracture aperture, length, or quantities increase. Still, it has different changes in three directions with the rise of the fracture dip, indicating the fractures lead to electrical anisotropy of the rock. Additionally, the resistivity index (RI) is strongly correlated to the water saturation (Sw). In the highly water-saturated region, the RI-Sw curve conforms to Archie's law. However, the RI-Sw curve bends in the lowly water-saturated region, revealing the so-called ‘non-Archie’ behavior. Hence, we established new saturation models for fractured carbonate rocks and use one of the saturation models to calculate water saturation for formation evaluation in Tahe Oilfield. The calculated results are more consistent with the oil test results, demonstrating the proposed saturation model is more applicable in the fractured formation. In summary, the pore-scale numerical simulation in fractured carbonate rocks provides a microscopic theoretical basis for electrical mechanism analysis and a water saturation model for log interpretation.

Pore throat structure and classification of Paleogene tight reservoirs in Jiyang depression, Bohai Bay Basin, China

The pore throat structure characteristics of Paleogene tight sandstone and sandy conglomerate in the Jiyang depression are studied using cast thin section, conventional mercury injection, constant rate mercury injection and micro CT scanning data, and a reservoir classification scheme based on pore throat structure parameters is established. The material composition and structural characteristics of tight reservoirs are analyzed by casting thin section data. The pore throat structure characteristics of tight reservoirs are studied by conventional mercury injection, constant rate mercury injection and micro CT scanning. Ten pore throat structure parameters are analyzed by cluster analysis. Based on the classification results and oil test results, the classification scheme of Paleogene tight reservoirs is established. The Paleogene tight reservoirs in the Jiyang depression have the characteristics of macropores and microthroats, with pores in micron scale, throats in nano-submicron scale, and wide variation of ratio of pore radius to throat radius. The permeability of the tight reservoir is controlled by throat radius, the smaller the difference between pore radius and throat radius, and the more uniform the pore throat size, the higher the permeability will be. The lower limits of average pore throat radius for the tight sandstone and tight sandy conglomerate to produce industrial oil flow without fracturing are 0.6 μm and 0.8 μm, respectively. Reservoirs that can produce industrial oil flow only after fracturing have an average pore-throat radius between 0.2–0.6 μm, and reservoirs with average pore throat radius less than 0.2 μm are ineffective reservoirs under the current fracturing techniques. Different types of tight sandstone and sandy conglomerate reservoirs are classified and evaluated, which are well applied in exploratory evaluation.

ISOLASI MINYAK ATSIRI BIJI BUAH PALA (Myristica fragrans Houtt) DAN PENGGUNAANNYA DALAM PEMBUATAN SABUN TRANSPARAN YANG DI KOMBINASIKAN DENGAN MINYAK JARAK

Nutmeg oil is an essential oil produced through the distillation process from dried nutmeg seeds. Nutmeg oil is usually obtained after the fat contained in it is removed first. Nutmeg oil is used in the pharmaceutical industry as a remedy for stomach aches and diarrhea. The development of castor oil in Indonesia has been increasingly widespread, but its utilization has not been maximized. To maximize the utilization of castor oil, castor oil is processed into soap. Castor oil is always used in the manufacture of transparent soap because castor oil can provide transparent properties to soap. Soap is a surfactant that is used with water for cleaning and washing. The purpose of this study was to use nutmeg essential oil as a comparison with castor oil in making transparent soap. The research method used is experimental. Making transparent soap using nutmeg essential oil as a comparison with castor oil, the amount of nutmeg essential oil and castor oil used was 5ml, 10ml, 15ml, 20ml. Tests of soap preparations made include organoleptic tests, water content tests, pH tests, mineral oil tests, high foam tests and free fatty acid/free alkali tests. From the results of the tests carried out, it can be concluded that the ratio of nutmeg oil and castor oil which has the best concentration is the concentration of 5ml jatropha: 15ml nutmeg. This comparison meets the requirements, namely the results of organoleptic tests that produce good shape, smell and color, the results of the water content test produce 5.5%, the pH test results produce 8. the mineral oil test results were negative, the foaming power and foam stability test results were fixed at 285 and the free alkali test results were 0.039

A study on the Identification of Jurassic paleo-oil reservoirs in the Mosuowan uplift, Junggar Basin

At present, some small to medium-sized oil and gas reservoirs have been discovered in Jurassic Lower Sangonghe Formation (J1s). Previous studies speculated that the Mosuowan uplift was an important gathering area for oil and gas migration during the middle and late Jurassic, and it had a large-scale oil accumulation. Due to the southern dipping of the basin during the Himalayan Period, the paleo-oil and gas reservoirs formed in the Mosuowan uplift in the early stage migrated from the south to the north and re-accumulated. In this paper, the grain fluorescence technique was used to measure the fluorescence intensity of hydrocarbons and free hydrocarbons in the particles of the reservoir by laser scanning analysis. The fluid stratigraphy analysis technique was used to remove the free natural gas and light hydrocarbons in the reservoir sandstone by vacuum desorption. The two techniques combined with gas measurement of total hydrocarbons, geochemical parameters and oil test results, identify the height of the ancient oil column, and clarify that most of the wells exist in the exploration layer. But the height of the column is generally small, and most of the single layers are less than 20m, indicating that the Mosuowan uplift does not form a broad-cover type of oil and gas reservoir. Most wells don’t have obvious ancient oil columns.

Application of Machine Learning in Transformer Health Index Prediction

The presented paper aims to establish a strong basis for utilizing machine learning (ML) towards the prediction of the overall insulation health condition of medium voltage distribution transformers based on their oil test results. To validate the presented approach, the ML algorithms were tested on two databases of more than 1000 medium voltage transformer oil samples of ratings in the order of tens of MVA. The oil test results were acquired from in-service transformers (during oil sampling time) of two different utility companies in the gulf region. The illustrated procedure aimed to mimic a realistic scenario of how the utility would benefit from the use of different ML tools towards understanding the insulation health index of their transformers. This objective was achieved using two procedural steps. In the first step, three different data training and testing scenarios were used with several pattern recognition tools for classifying the transformer health condition based on the full set of input test features. In the second step, the same pattern recognition tools were used along with the three training/testing scenarios for a reduced number of test features. Also, a previously developed reduced model was the basis to reduce the needed number of tests for transformer health index calculations. It was found that reducing the number of tests did not influence the accuracy of the ML prediction models, which is considered as a significant advantage in terms of transformer asset management (TAM) cost reduction.

Quantitative prediction of seismic rock physics of hybrid tight oil reservoirs of the Permian Lucaogou Formation, Junggar Basin, Northwest China

Tight oil exploration and development in the Jimusar Sag of southeastern Junggar Basin of Northwest China has led to development of two reservoir segments in the Lucaogou Formation. The upper segment is mostly carbonate rocks, while the lower segment consists mainly of fine-grained clastic rocks. Both segments are mixed with clay, sand, and dolomite, which makes it difficult to identify the lithology and predict the porosity using a traditional seismic rock physics template (RPT). We analyzed the trends of ultrasonic P-wave impedance and Vp/Vs of laboratory data changing with lithology, porosity, and total organic carbon (TOC) content. Our data provide parametric sensitivity regarding porosity and TOC content by use of a bi-material-matrix–double-inclusion concentration model. We propose a modified RPT of P-wave impedance versus Vp/Vs with three end members (sandstone, dolostone, and mudstone) to predict porosity and lithology of reservoirs. We also present a new RPT of density versus Poisson ratio with the same three end members to predict TOC content and lithology of source rocks. These were validated by comparisons with laboratory measurements and log data. We inverted the porosity, TOC content, and thickness of the upper reservoir segment in the Lucaogou Formation of the J17 work area of Jimusar Sag by using the new RPT quantitative prediction method. The inversion results were identical to oil testing results, supporting the viability and effectiveness of the new RPTs.

Factorial analysis for ageing assessment of in‐service transformers

Condition monitoring data in the form of oil test results are important for transformer ageing assessment and asset management. Analysis of variance-based factorial analysis was demonstrated as a data mining technique for identifying the influence of different factors on transformer ageing. Using a UK oil test database of 33/11(or 6.6) kV transformers, studies were performed to assess the influence of in-service age, manufacturer, year of manufacture, load and environment on the measured oil acidity. This methodology can reduce the workload of manually performing pairwise comparisons to understand the influence of each factor. In addition, it can also consider any interaction effect present among the factors. As a demonstration, influence of the year of manufacture on oil acidity was revealed, which was confirmed by identifying a change in design for late 1960s units and an early degradation phenomenon for late 1980s units.

An early degradation phenomenon identified through transformer oil database analysis

Ageing of large transformer fleets is a challenge for utilities. To assess the condition of existing transformer fleets, transformer oil is commonly tested for multiple parameters and the data are recorded in large databases for subsequent interpretation. Through analysing multiple databases including oil test results and individual transformer details pertaining to UK in-service transformers operating at primary voltage levels of 33, 132, 275 and 400 kV, population analyses revealed a generic early degradation phenomenon as indicated by an early peak in acidity and 2-FAL trends with in-service age. By exploring the phenomenon from manufacturer, loading and oil chemistry change perspectives, results suggested that the early degradation was most likely due to an oil chemistry change resulting from hydrotreatment oil refining method introduced in the late 1980s. Judging from the faster degradation trend of the affected transformers, a separate asset management strategy may be needed.