Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

Application of Seismic Inversion Using Logging Data as Constraints in Coalfield

Seismic inversion and basic theory are briefly presented and the main idea of this method is introduced. Both non-linear wave equation inversion technique and Complete Utilization of Samples Information (CUSI) neural network analysis are used in lithological interpretation in Jibei coal field. The prediction results indicate that this method can provide reliable data for thin coal exploitation and promising area evaluation.

Logging Data High-Resolution Sequence Stratigraphy

The recognition and contrast of bed sets in parasequence is difficult in terrestrial basin high-resolution sequence stratigraphy. This study puts forward new methods for the boundary identification and contrast of bed sets on the basis of manifold logging data. The formation of calcareous interbeds, shale resistivity differences and the relation of reservoir resistivity to altitude are considered on the basis of log curve morphological characteristics, core observation, cast thin section, X-ray diffraction and scanning electron microscopy. The results show that the thickness of calcareous interbeds is between 0.5 m and 2 m, increasing on weathering crusts and faults. Calcareous interbeds occur at the bottom of a distributary channel and the top of a distributary mouth bar. Lower resistivity shale （4-5 Ω · m） and higher resistivity shale （〉 10Ω·m） reflect differences in sediment fountain or sediment microfacies. Reservoir resistivity increases with altitude. Calcareous interbeds may be a symbol of recognition for the boundary of bed sets and isochronous contrast bed sets, and shale resistivity differences may confirm the stack relation and connectivity of bed sets. Based on this, a high-resolution chronostratigraphic frame- work of Xi-1 segment in Shinan area, Junggar basin is presented, and the connectivity of bed sets and oil-water contact is confirmed. In this chronostratigraphic framework, the growth order, stack mode and space shape of bed sets are qualitatively and quantitatively described.

Lateral Contrast and Prediction of Carboniferous Reservoirs Using Logging Data in Tahe Oilfield,Xinjiang,China

Valuable industrial oil and gas were discovered in the formations of Ordovician, Carboniferous and Triassic of the Tahe （塔河） oilfield, Xinjiang （新疆）, China. The Carboniferous formations contain several oil- and gas-bearing layers. The lateral distribution of Carboniferous reservoir is unstable, and thin layers are crossbedded. This makes it difficult to do lateral formations＇ contrast and reservoir prediction, so it is necessary to develop a method that can achieve reservoir lateral contrast and prediction by using multi-well logging data and seismic data. To achieve reservoir lateral contrast and prediction at the Carboniferous formations of the Tahe oilfield, processing and interpretation of logging data from a single well were done first. The processing and interpretation include log pretreatment, en- vironmental correction and computation of reservoir＇s parameters （porosity, clay content, water saturation, etc.）. Based on the previous work, the data file of logging information of multi-well was formed, and then the lateral distribution pictures （2D and 3D pictures of log curves and reservoir parameters） can be drawn. Comparing multi-well＇s logging information, seismic profiles and geological information （sedimentary sign）, the reservoir of the Carboniferous in the Tahe oilfield can be contrasted and pre- dicted laterally. The sand formation of Carboniferous can be subdivided. The results of reservoir contrast and prediction of the Carboniferous formations show that 2D and 3D pictures of multi-weU reser- voir parameters make the lateral distribution of reservoir and oil-bearing sand very clear, the connectedness of the reservoir of neighboring wells can be analyzed, and five sand bodies can be identified based on the reservoir＇s lateral distribution, geological information and seismic data.

A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

Application of 3D Seismic Data Inversion to Coal Mining Prospecting

Seismic inversion is one of the most important methods for lithological prospecting . Seismic data with lowresolution is converted into impedance data of high resolution which can reflect the geological structure by inversionThe inversion technique of 3D seismic data is discussed from both methodological and theoretical aspects, and the in-version test is also carried out using actual logging data. The result is identical with the measured data obtained fromroadway of coal mine. The field tests and research results indicate that this method can provide more accurate data foridentifying thin coal seam and minor faults.

Predicting formation lithology from log data by using a neural network

In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.

Design and Implementation of Log Data Analysis Management System Based on Hadoop

With the rapid development of the Internet,many enterprises have launched their network platforms.When users browse,search,and click the products of these platforms,most platforms will keep records of these network behaviors,these records are often heterogeneous,and it is called log data.To effectively to analyze and manage these heterogeneous log data,so that enterprises can grasp the behavior characteristics of their platform users in time,to realize targeted recommendation of users,increase the sales volume of enterprises’products,and accelerate the development of enterprises.Firstly,we follow the process of big data collection,storage,analysis,and visualization to design the system,then,we adopt HDFS storage technology,Yarn resource management technology,and gink load balancing technology to build a Hadoop cluster to process the log data,and adopt MapReduce processing technology and data warehouse hive technology analyze the log data to obtain the results.Finally,the obtained results are displayed visually,and a log data analysis system is successfully constructed.It has been proved by practice that the system effectively realizes the collection,analysis and visualization of log data,and can accurately realize the recommendation of products by enterprises.The system is stable and effective.

Design and Implementation of a Photovoltaic Data Acquisition System for Some Meteorological Variables

In this paper we have developed a data logging and monitoring system, we validated the system by comparing the result from it with the existing one and found that the system performs slightly better than the existing work in the same area. This implies that the data logger and monitoring system is good and can be used to monitor solar energy variables even at the comfort of our homes. We fitted a model to the generated data and found that the meteorological variables considered accounted for 99.88% of the power output in the rainy seasons while 0.12% of the variation was not explained due to other factors. Solar panels inclined at an angle of 5° (Tilt) and facing South Pole perform optimally.

Alarm Log Data Augmentation Algorithm Based on a GAN Model and Apriori

The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data.Due to the strong rule associations inherent in alarm log data,existing data augmentation algorithms cannot obtain good results for alarm log data.To address this problem,this paper introduces a new algorithm for augmenting alarm log data,termed APRGAN,which combines a generative adversarial network(GAN)with the Apriori algorithm.APRGAN generates alarm log data under the guidance of rules mined by the rule miner.Moreover,we propose a new dynamic updating mechanism to alleviate the mode collapse problem of the GAN.In addition to updating the real reference dataset used to train the discriminator in the GAN,we dynamically update the parameters and the rule set of the Apriori algorithm according to the data generated in each epoch.Through extensive experimentation on two public datasets,it is demonstrated that APRGAN surpasses other data augmentation algorithms in the domain with respect to alarm log data augmentation,as evidenced by its superior performance on metrics such as BLEU,ROUGE,and METEOR.

Transfer learning for well logging formation evaluation using similarity weights

Machine learning has been widely applied in well logging formation evaluation studies.However,several challenges negatively impacted the generalization capabilities of machine learning models in practical imple-mentations,such as the mismatch of data domain between training and testing datasets,imbalances among sample categories,and inadequate representation of data model.These issues have led to substantial insufficient identification for reservoir and significant deviations in subsequent evaluations.To improve the transferability of machine learning models within limited sample sets,this study proposes a weight transfer learning framework based on the similarity of the labels.The similarity weighting method includes both hard weights and soft weights.By evaluating the similarity between test and training sets of logging data,the similarity results are used to estimate the weights of training samples,thereby optimizing the model learning process.We develop a double experts’network and a bidirectional gated neural network based on hierarchical attention and multi-head attention(BiGRU-MHSA)for well logs reconstruction and lithofacies classification tasks.Oil field data results for the shale strata in the Gulong area of the Songliao Basin of China indicate that the double experts’network model performs well in curve reconstruction tasks.However,it may not be effective in lithofacies classification tasks,while BiGRU-MHSA performs well in that area.In the study of constructing large-scale well logging processing and formation interpretation models,it is maybe more beneficial by employing different expert models for combined evaluations.In addition,although the improvement is limited,hard or soft weighting methods is better than unweighted(i.e.,average-weighted)in significantly different adjacent wells.The code and data are open and available for subsequent studies on other lithofacies layers.

A formation pressure prediction method based on tectonic overpressure

Traditional formation pressure prediction methods all are based on the formation undercompaction mechanism and the prediction results are obviously low when predicting abnormally high pressure caused by compressional structure overpressure.To eliminate this problem,we propose a new formation pressure prediction method considering compressional structure overpressure as the dominant factor causing abnormally high pressure.First,we establish a model for predicting maximum principal stress,this virtual maximum principal stress is calculated by a double stress field analysis.Then we predict the formation pressure by fitting the maximum principal stress with formation pressure. The real maximum principal stress can be determined by caculating the sum of the virtual maximum principal stresses.Practical application to real data from the A1 and A2 wells in the A gas field shows that this new method has higher accuracy than the traditional equivalent depth method.

Distinguishing volcanic lithology using Self-Organizing Map

Self-Organizing Map is an unsupervised learning algorithm.It has the ability of self-organization,self-learning and side associative thinking.Based on the principle it can identified the complex volcanic lithology.According to the logging data of the volcanic rock samples,the SOM will be trained,The SOM training results were analyzed in order to choose optimally parameters of the network.Through identifying the logging data of volcanic formations,the result shows that the map can achieve good application effects.

Extending Auditing Models to Correspond with Clients’ Needs in Cloud Environments

The user control over the life cycle of data is of an extreme importance in clouds in order to determine whether the service provider adheres to the client’s pre-specified needs in the contract between them or not, significant clients concerns raise on some aspects like social, location and the laws to which the data are subject to. The problem is even magnified more with the lack of transparency by Cloud Service Providers (CSPs). Auditing and compliance enforcement introduce different set of challenges in cloud computing that are not yet resolved. In this paper, a conducted questionnaire showed that the data owners have real concerns about not just the secrecy and integrity of their data in cloud environment, but also for spatial, temporal, and legal issues related to their data especially for sensitive or personal data. The questionnaire results show the importance for the data owners to address mainly three major issues: Their ability to continue the work, the secrecy and integrity of their data, and the spatial, legal, temporal constraints related to their data. Although a good volume of work was dedicated for auditing in the literature, only little work was dedicated to the fulfillment of the contractual obligations of the CSPs. The paper contributes to knowledge by proposing an extension to the auditing models to include the fulfillment of contractual obligations aspects beside the important aspects of secrecy and integrity of client’s data.

Division of high resolution sequence stratigraphy units with wavelet transform of logs in Dagang Oilfield

Division of high resolution sequence stratigraphy units based on wavelet transform of logging data is found to be good at identifying subtle cycles of geological process in Kongnan area of Dagang Oilfield. The anal- ysis of multi-scales gyre of formation with 1-D continuous Dmey wavelet transform of log curve （GR） and I-D discrete Daubechies wavelet transform of log curve （Rt） all make the division of sequence interfaces more objec- tive and precise, which avoids the artificial influence with core analysis and the uncertainty with seismic data and core analysis.

Development of an automated system for continuous monitoring of powered roof support in longwall panel

Described the development of an Intrinsically Safe System for continuous monitoring of load and convergence of powered roof supports installed at Iongwall faces. The system developed for monitoring of behavior of a powered support in a mechanized Iongwall sublevel caving face. The logging system can be programmed for logging the data from the sensors at different logging intervals ranging from 16 h to 1 ms for logging variation in hydraulic pressures in legs and convergence of the support during progressive face advance. For recording dynamic loads, the data logger can be programmed to start fast logging, say at 10 ms intervals, when the pressure in a leg reaches a pre-specified threshold value, and continue fast logging until the pressure drops below this threshold value. This fast logging automatically stops when the pressure drops below this threshold value.

A novel clustering and supervising users' profiles method

To better understand different users' accessing intentions, a novel clustering and supervising method based on accessing path is presented. This method divides users' interest space to express the distribution of users' interests, and directly to instruct the constructing process of web pages indexing for advanced performance.

Neural Network Applications in Petroleum Exploration Based on Statistical Space Mapping

In this paper, we propose the statistical space mapping thought and classify the seismic body space throughlithology space clustering combining to the actual application background of petroleum exploration. A new method ofstratum petroleum recognition based on neural network was set up through the foundation of the data mapping relationbetween log and seismic body. It can break a new path for recognition petroleum using both log and seismic data. Andthis method has been validated in the practical data analysis in Liaohe oil field.

Clay Minerals Properties as Downhole Formation Pressure Indicator

The successful estimation of formation pressures （or formation pore gradient） is fundamental and the basis for many engineering works including drilling and oilfield development planning. Common log data are used for formation pressure calculation. Modern techniques for pressure prediction have several disadvantages, notably, incorrect account of the downhole nonsteady thermal field and clay mineral composition. We propose a way to overcome listed shortcomings： a technique for thermal field proper account while formation pressure estimation and a petrophysical model, which reflects relationships between clay minerals composition and rock properties, derived from log data.

An integrated foot transducer and data logging system for dynamic assessment of lower limb exerted forces during agricultural machinery operations

Agricultural machinery typically requires lower limb actuation forces for operations such as treadling,pedaling and tractor based.However,limited systems exist for assessment of such forces that have ergonomic influence.This study,therefore developed and evaluated a single board computer integrated foot transducer(IFT)and autonomous data logging and visualization systemtomonitor dynamic lower limb exerted forces.The systemconsists of custom developed load sensors sandwiched into foot shaped units that fit operator's both feet.Stamped forces at crank angles for operations typical to pedaling while at height(above ground level)for operation representing typical treadling operations were recorded on-board amemory card and displayed on a liquid crystal display.Evaluations were conducted by imposing external loads that significantly increased(p b 0.05)the foot exerted forces.Force trends were periodic with peaks of 73,85,110.5 and 145.4 N for left foot and 41,50,131.7 and 145.4 N for right foot at loads of 10,30,50 and 70 N,respectively during pedaling operations.Similarly,the left lower actuation limb exerted forces of 139,249 and 255 N at 5,10 and 15 N of imposed loads,respectively during treadling operation.System was also evaluated for tractor operations and exerted forces ranged from 92 to 164 and 107–176 N for clutch pedal engagement at lower to higher tractor speeds on farm and tarmacadam roads,respectively.Similarly,for brake pedal engagement,such forces ranged from106 to 173 and 120–204 N on farm and tarmacadamroads.These forces varied significantly at different forward speeds.Results suggest potential of such system for foot exerted force assessments typical to agricultural machinery systems in real field.Designsmay be evaluated or reconsidered tominimizemusculoskeletal disorder risks during prolonged operations.Work-rest schedules protocols can be developed by ergonomists for safe,efficient and comfortable operations.