Category Archives: Data Mining
Local-feature and global-dependency based tool wear prediction using deep learning | Scientific Reports – Nature.com
In this section, an experiment was designed to test the performances of our proposed LFGD-TWP method.
The machining experiment was carried out in milling operation and the experimental equipment and materials used in this experiment are shown in Table 1. The cutting force acquisition system mainly consists of sensor, transmitter, receiver and PC. The sensor and signal transmitter are integrated into a toolholder, which can directly collect the force data during machining and send it out wirelessly. The signals are collected at a frequency of 2500Hz. The collected data from sensor is transmitted wirelessly to receiver, which in turn transmits the data to PC via USB cable. The signal collection process is shown in Fig.6.
The Anyty microscope was fixed inside the machine tool as shown in Fig.7. The coordinate where image of tool wear can be clearly taken is recorded into the CNC so that the spindle can move to this fixed position for wear measurement after each milling. This measurement method avoids the errors caused by repeated removal and installation of cutters, which improves the efficiency and accuracy of tool wear measurement. A sample photo of the microscope is shown in Fig.8.
A sample photo of tool wear.
Orthogonal experimental method was adopted in this paper in order to test the performances of our method under multiple working conditions. Tool wear experiments are conducted using nine cutters under nine different cutting parameters. The 9 cutters are marked as C1, C2,, C9. The milling parameters were set as shown in Table 2. The cutting width was fixed at 7mm. Each row in the table corresponds to a new cutter. Every 1000mm cutting was a cut and the tool wear was measured after every cut. Replace the cutter and cutting parameters when the previous tool wear exceeds the threshold or the cutter is broken.
The data acquisition files have three columns, corresponding to: bending moment in two directions (x, y) and torsion. Each cutter has a corresponding wear file. The wear file records the wear values of the four flutes corresponding to each cut. The cutting quality will become poor if the wear value of any edge exceeds a certain value. Therefore, this paper takes the maximal flank wear of all flutes as target.
Considering the multisensory input contain three channels, the bending moment in X direction is used as an example to illustrate the data preparation process in this paper. Firstly, the original signal of each cut is truncated to obtain the valid data segment containing 10,240 recorded values in the middle part of each signal. Finally, the data is equally divided into 10 segments based on practice, denoted as (X_{fx} = left[ {X_{1} ,X_{2} ,...,X_{10} } right]).
The maximum level of decomposition in DWT is related to the length of signals and the chosen wavelet. In this paper, db5 is used for decomposition and we select the optimal level of decomposition by comparing the performance under different levels of decomposition. Decomposition level 3, 4, 5 and 6 were chosen for comparison in this paper. The results showed that level 5 had the best performance. Therefore,(X_{1} ,X_{2} ,...,X_{10}) are converted to multi-scale spectrogram images respectively by 5-level wavelet decomposition using db5 based on the practice, denoted as (WS = [ws_{1} ,ws_{2} ,...,ws_{10} ]) where (ws = [c_{1} ,c_{2} ,...,c_{6} ]) with the length of [512, 256, 128, 64, 32, 32] is multi-scale vectors corresponding to each segment.
For each segment, 1D-CNNs are used to extract single-scale features from (c_{1} ,c_{2} ,...,c_{6}) respectively. The structure and parameters of the model are shown in Table 3.
The activation function of the convolution layer is ReLU. Every convolution layer of (c_{1} ,c_{2} ,c_{3} ,c_{4}) is followed by a max-pooling layer with region 12 to compress generated feature maps. The input channel of the model is set to 3 because of the three-channel sensory data.
After the single-scale Feature Extraction by 1D-CNNs and the concatenation of single-scale Features, a feature image of size ({32} times {6} times 32) is obtained, which is used as the input of our multi-scale correlation feature extraction model. Finally, the local feature size of each segment after automatic extraction is 150.
In this case, the dimension of automatic feature vector is 50, and the dimension of manual feature vector is 30. The adopted manual features are shown in Table 4. Therefore, the dimension of the hybrid features of each segment is 80.
The number of segments is T=10 so that the shape of the input sequence of Global Time Series Dependency Mining Model is 8010. The Mean Squared Error (MSE) was selected as the model loss during model training. An Adam optimizer32 is used for optimization in this paper and the learning rate is set to be 0.001. MSE was calculated on test data set for the models having one, two, and three layers and 100, 200, 300, 400, 500 hidden units. The results show that the most accurate model contained 2 layers and 300 hidden units in LSTM models and 400 hidden units in FC-Layer. In order to improve the training speed and alleviate the overfitting issues, we apply batch normalization (BN)33 to all convolution layers of Single-Scale Feature Extraction Model, and apply the dropout method34 to the fully connected layer. To get a relatively optimal dropout value, we set different values to train the model, i.e., p=0, p=0.25, p=0.5, p=0.75. Where p is the probability of an element to be zeroed. The results show that the dropout setting of 0.5 gives a relatively optimal result. After updating the parameters of the model with the training data, the trained model is applied on the testing data to predict tool wear.
In order to quantify the performance of our method, mean absolute error (MAE) and root mean squared error (RMSE) are adopted as measurement indicators to evaluate regression loss. The equations of MAE and RMSE over n testing records are given as follows:
$$ MAE = frac{1}{n}sumlimits_{i = 1}^{n} {left| {y_{i} - hat{y}_{i} } right|} , $$
(5)
$$ RMSE = sqrt {frac{1}{n}sumlimits_{i = 1}^{n} {(y_{i} - hat{y}_{i} )^{2} } } , $$
(6)
where (y_{i}) is predicted value and (hat{y}_{i}) is true value.
To analyze the performance of all our methods, cross validation is used to test the accuracy of the model in this paper. Eight cutter records are used as training sets and the rest one is used as testing set, until all cutters are used as testing set. Forexample, records of cutters C2, C3, , C9 are used as the training sets and records of cutter C1 are used as the testing set, the testing case is denoted as T1. Then the records of cutter C2 are used as the testing set, and the records of the rest cutter are used as the training sets, the testing case is denoted as T2. The rest can be done in the same manner. Nine different testing cases are shown in Table 5.
To mitigate the effects of random factors, each testing case is repeated 10 times and the average value is used as the result of the model. Moreover, in order to demonstrate the effectiveness of the hybrid features in this paper, two models are trained, namely the network with hybrid features and the network with automatic features only. The results of each testing cases are shown in Table 6.
It can be seen from Table 6 that our proposed LFGD-TWP achieves low regression error. In most cases, the model with hybrid features performs better than the model with automatic features only. By calculating the average performance improvement, we can reach a 3.69% improvement in MAE and a 2.37% improvement in RMSE. To qualitatively demonstrate the effectiveness of our model, the predicted tool wears of testing case T2 and T7 are illustrated in Fig.9. It can be seen from Fig.9 that the closer to the tool failure zone, the greater the error. The reason for this may be that the tool wears quicker at this stage, resulting in a relatively small number of samples. Or it could be that the signal changes more drastically and the noise is more severe due to the increasing tool wear, leading to greater error.
Tool wear predicted by LFGD-TWP.
Two statistics are adopted to illustrate the overall prediction performance and generalization ability of the model under different testing cases: mean and variance. Mean is the average value of the results under different testing cases. Obviously, it indicates the prediction accuracy of the method. Variance measures how far each result is from the mean and thus measures variability from the average or mean. It indicates the stability of generalization under different testing cases. The equations of mean and variance of two measurement indicators over n testing cases are given as follows:
$$ Mean = overline{r} = frac{1}{n}sumlimits_{i = 1}^{n} {r_{i} } , $$
(7)
$$ Variance = frac{1}{n}sumlimits_{i = 1}^{n} {left( {r_{i} - overline{r}} right)^{2} } , $$
(8)
where (r_{i}) is the mean value of the results for each testing case.
The definition of mean and variance shows that the smaller their values are, the better performance of the model will be. In our proposed method, the means of MAEs and RMSEs are 7.36 and 9.65, and the variances of MAEs and RMSEs are 0.95 and 1.65.
Other deep learning models are used to compare model performance with the proposed LFGD-TWP. They are CNN24, and LSTM30 and CNN-BiLSTM19, and the structure of these models are shown as follows.
Structure of CNN model in brief: The input of CNN model is the original signal after normalization, and the signal length is 1024. The input channel of the model is set to 3 because of the three-channel sensory data. CNN model has 5 convolution layers. Each convolutional layer has 32 feature maps and 14 filters which is followed by a max-pooling with region 12. Then flatten the feature maps. Finally, it is followed by a fully connected layer, which has 250 hidden layer units. The dropout operation with probability 0.5 is applied to the fully connected layer. The loss function is MSE, the optimizer function is Adam, the learning rate is set to be 0.001, which are kept the same as the proposed model. The means of MAEs and RMSEs are 12.64 and 16.74, and the variances of MAEs and RMSEs are 10.74 and 18.90.
Structure of LSTM model in brief: The model is of type many to one. The input of LSTM is the manual features in Table 4. Therefore, an LSTM cell has an input dimension of 30. The MAE and RMSE values were calculated for models with one, two, and three layers and 100, 200, 300, 400 hidden units. Therefore, 12 structures of an LSTM model were constructed for the most accurate model. Also, the timesteps are 10, the loss function is MSE, the optimizer function is Adam, the learning rate is set to be 0.001, which are kept the same as the proposed model. The results show that the most accurate model contained 2 layers and 200 hidden units. The means of MAEs and RMSEs are 10.48 and 13.76, and the variances of MAEs and RMSEs are 5.12 and 9.28.
Structure of CNN-BiLSTM model is shown in Ref.19, and the input of this model is the original signal after normalization. The means of MAEs and RMSEs of this model are 7.85 and 10.24, and the variances of MAEs and RMSEs are 2.71 and 5.06. Comparison results of our method (LFGD-TWP) and popular models are shown in Table 7. Compared to the most competitive result achieved by CNN-BiLSTM, the proposed model achieves a better accuracy owing to the multi-frequency-band analysis structure. Further, it can be seen that the proposed model achieves lower variances in MAE and RMSE. It means that the proposed model has better overall prediction performance and better stability of generalization under different testing cases by comparing the variance of the results.
To further test the performance of our proposed method, we additionally use the PHM2010 data set35, which is a widely used benchmark. The machining experiment was carried out in milling operation and the experimental equipment and materials used in this experiment are shown in Ref.19. The running speed of the spindle is 10,400 r/min; the feed rate in x-direction is 1555mm/min; the depth of cut (radial) in y-direction is 0.125mm; the depth of cut (axial) in z-direction is 0.2mm. There are 6 individual cutter records named C1, C2,, C6. Each record contains 315 samples (corresponding to 315 cuts), and the working conditions remain unchanged. C1, C4, C6 each has a corresponding wear file. Therefore, C1, C4, C6 are selected as our training/testing dataset. Also, cross validation is used to test the accuracy of the model and the results are shown in Fig.10.
Tool wear (PHM2010) predicted by LFGD-TWP.
In our proposed method, the mean of MAEs is 6.65, the mean of RMSEs is 8.42. Compared with the mean value of MAEs (6.57) and RMSEs (8.1) in Ref.19. The reason for the slightly poor performance may be that in order to enhance the adaptability to multiple working conditions, the architecture of the model is more complex, which leads to overfitting. Although the proposed architecture might overfit the PHM2010 case, the complexity of the architecture ensures that more complex scenarios like the test cases in the paper can be handled.
Read more:
Plant health index as an anomaly detection tool for oil refinery processes | Scientific Reports – Nature.com
PHI has been designed to capture and assess the condition of equipment during its life cycle. Thus, it may be utilized in data-driven condition-based maintenance and helps in predicting failures and malfunctions20.
Data Acquisition refers to collection of historical data for a long duration for training a predictive model under normal operating conditions. It is preferable that collected data contains various operating modes and may also include abnormal conditions and operational variations that result from, for example, aging of equipment, fouling, and catalyst deactivation.
The training datasets are collected in real-time directly from the sensors associated with the plant components. The datasets capture the three operational modes; i.e. startup mode, normal operating mode, and shutdown mode. These modes can be subdivided into more detailed modes in some circumstances.
Although the parameters possess a strong correlation, the time lag appears among them may lead to the inability to extract the relationship. The explanation for the time delay in parameters with physical relationships is that it takes time to reach a steady-state once certain changes occur and migrate from one portion to another. However, if the parameters have a strong association, if they change over time, the correlation coefficient may be modest, resulting in errors during the grouping procedure. We employed a dynamic window for sampling which examines the temporal lag among parameters to aid in the effective grouping of variables with a strong link.
The time lag was dealt with using cross correlation. For a delay duration of (t_{d}), Eq.(9) defines the coefficient for cross correlation between two parameters (A) ((a_{0}), (a_{1} , ldots , a_{M})) and (B) ((b_{0}), (b_{1} , ldots , b_{M}))21. The averages of (A) and (B) are (mu_{A}) and (mu_{A}), respectively.
$${upgamma }_{AB} left( {t_{d} } right) = frac{{mathop sum nolimits_{i = 0}^{M - 1} left( {a_{i} - {upmu }_{A} } right)*left( {b_{{i - t_{d} }} - {upmu }_{B} } right)}}{{sqrt {mathop sum nolimits_{i = 0}^{M - 1} left( {a_{i} - mu_{A} } right)^{2} } sqrt {mathop sum nolimits_{i = 0}^{M - 1} left( {b_{{i - t_{d} }} - mu_{B} } right)^{2} } }}$$
(9)
Grouping parameters aims to remove elements that don't provide meaningful data and to limit the number of parameters needed to adequately observe a component. The correlation coefficient employed as a reference for this grouping procedure is calculated for each pair of variables using Eq.(10), and if it exceeds a specified threshold, the variable is included in the training set; otherwise, it is discarded21.
$$rho_{AB} = frac{1}{M}mathop sum limits_{i = 0}^{M - 1} left( {frac{{a_{i} - {upmu }_{A} }}{{{upsigma }_{A} }}} right)left( {frac{{b_{i} - {upmu }_{B} }}{{{upsigma }_{B} }}} right)$$
(10)
where (rho_{AB}) is the correlation coefficient among (A) and (B), and (sigma_{A}) and (sigma_{B}) are their standard deviations.
There are three possible ways to group the parameters: Relational grouping (tags with the same patterns are grouped together), Manual grouping (each group possesses all of the tags), and Success Tree based grouping. The cut-off value of the correlation coefficients is known as group sensitivity. The grouping will become more precise if the group sensitivity is larger. When data is compressed during grouping, the Group Resolution (Shrink) feature is employed. If a tag has 1000 samples and the compression ratio is 100, the samples will be compressed to 100 and the missing information will be filled in by the Grid Size. Major significance of compression includes reduced data storage, data transfer time, and communication bandwidth. Time-series datasets frequently grow to terabytes and beyond. It is necessary to compress the datasets collected for attaining most effective model while preserving available resources.
Preprocessing of collected data is indispensable to ensure the accuracy of the developed empirical models, which are sensitive to noise and outliers. The selection of the sampling rate is also crucial, mainly because for the oil refinery processes the sampling rate (measurement frequency) is much faster than the process dynamics. In the current implementation, low pass frequency filtering with Fourier analysis was used to eliminate outliers, a 10min sampling rate was selected, and the compression rate (Group resolution or shrink) was set at 1000. Moreover, Kalman filter was applied to ensure robust noise distribution of collected data5. Another important preprocessing step is grouping. First, the useful information of the variables is grouped together. It helps to remove redundant variables that do not have useful information. It also reduces the number of variables required for monitoring the plant properly. Finally, the available information must be appropriately compressed via the transformation of high-dimensional data sets into low-dimensional features with minimal loss of class separability21. The maximum tags per group is limited to 51 in this simulation and success tree-based grouping is used in most of the cases. The minimum value of the correlation coefficient, (rho) is set to 0.20 and the group sensitivity was set to 0.90. Higher the group sensitivity will be more accurate the grouping.
Kernel regression is a well-known non-parametric method for estimating a random variable's conditional expectation22,23,24,25. The goal is to discover a non-linear relationship of the two random variables. When dealing with data that has a skewed distribution, the kernel regression is a good choice to use. This model determines the value of the parameter by estimating the exemplar observation and weighted average of historical data. The Kernel function is considered as weights in kernel regression. It is a symmetric, continuous, and limited real function that integrate to 1. The kernel function can't have a negative value. The NardarayaWatson estimator given by Eq.(11) is the most concise way to express kernel regression estimating (y) with respect to the input (x)21,23,24.
$$hat{y} = frac{{mathop sum nolimits_{i = 1}^{n} left[ {Kleft( {X_{i} - x} right)Y_{i} } right]}}{{mathop sum nolimits_{i = 1}^{n} Kleft( {X_{i} - x} right)}}$$
(11)
The selection of appropriate kernel for the situationislimited by practical and theoretical concerns. Reported Kernels are Epanechnikov, Gaussian, Quartic (biweight), Tricube (triweight), Uniform, Triangular, Cosine, Logistics, and Sigmoid 25. In the current implementation of PHI, three types of the kernel regression are provided: Uniform, Triangular, and Gaussian, which are defined as:
Uniform Kernel (Rectangular window): (Kleft( x right) = frac{1}{2}; where left| x right| le 1)
Triangular Kernel (Triangular window): (Kleft( x right) = 1 - left| x right|; where left| x right| le 1)
Gaussian Kernel: (Kleft( x right) = frac{1}{{sqrt {2pi } }}e^{{ - frac{{x^{2} }}{2}}})
The default is the Gaussian kernel which proved to be the most effective kernel for the current implementation.
PHI monitors plant signals, derives actual values of operational variables, compares actual values with expected values predicted using empirical models, and quantifies deviations between actual and expected values. Before positioning it to monitor plant operation, PHI should be first trained to predict the normal operating conditions of a process. Developing the empirical predictive model is based on a statistical learning technique consisting of an execution mode and a training mode. Methods and algorithms used in both modes of the PHI system are shown in Fig.9.
Algorithms of the PHI 26.
In the training mode, statistical methods are used to train the model using past operating data. The system identifies possible anomalies in operation for the execution mode by inspecting the discrepancies between values predicted by the empirical model and actual online measurements. For example, if a current operating condition approaches the normal condition, the health index is 100%. As opposed, if an operating condition approaches the alarm set point, the health index will be 0%. On the other hand, and in terms of process uncertainty, the health index is characterized by the residual deviations; the health index is 100% if a current operating condition is the same as the model estimate (i.e., the residual is 0.0), and is 0% if the operating conditions are far enough from the model estimate (i.e., residual is infinity). The overall plant index is a combination of the above two health indices. Details of the method are presented in21 and26 and presented as an improved statistical learning framework described below.
The framework of PHI is shown in Fig.10. The sequence of actions in the training mode is as follow:
Acquisition of historical data in the long term.
Data preprocessing such as filtering, signal compression, and grouping.
Development of the statistical model.
Evaluation of Health Index.
On the other hand, the sequence of actions in the execution mode is as follows:
Acquisition of real-time data.
Calculation of expected value from the model.
Calculation of residuals.
The decision of process uncertainty.
Calculation of PHI.
In the execution phase, first step is to gather real-time data from the sensor signals and compare this information with the model estimates. Based on the comparison, the residuals between the model estimates and the real time measurements are evaluated. These residuals are used to predict the abnormalities in the plant. Suppose that the online values are [11 12 13 9 15] and the model estimates [11 12 13 14 15], then the estimated residuals will be [0 0 0 5 0]. These values are used in evaluating the process uncertainty (healthiness) by applying Eq.(2). On the other hand, process margins refer to the differences between alarms/trips and the operational conditions, which are evaluated using Eq.(1). An early warning is generated when an abnormal process uncertainty is observed earlier than a process margin. The process margins and process uncertainties are combined in overall health indices using Eq.(3).
The PHI system has been developed using MATLAB. A modular approach has been used so that modifications may be easily introduced, and new algorithms may be added, integrated, and tested as independent modules. This approach was found quite appropriate for research and development purposes. Moreover, the PHI system is delivered as executable MATLAB files.
The main features and functionalities of PHI are (1) detecting the process uncertainty, in terms of a health index, for individual signals as well as for an entire plant, (2) warning anomalies in health indices, and (3) customized user interfaces and historians. Furthermore, since the PHI separately deals with safety-related and performance-related health indices, users can have appropriate decision-making in terms of their situation.
PHI system is a clientserver-based architecture, as shown in Fig.11. The server side is divided into the core modules necessary to build the PHI functionality and PRISM, a real-time BNF (Breakthrough and Fusion) technology database. The clients are divided into the standard client and the web-based client. Figure12 shows the main display of the PHI client. All of these functions bridge the information of the server-side with users.
Server architecture of the PHI system 26.
Example display of the PHI indicating the (a) overall plant health index and the health indices of the (b) reaction and (c) stripper sections.
The results of the PHI can be monitored through the client computer, which has the following main features:
Index display: the default display shows the index in percent of the topmost groups, including the trend. The index of other subsystems can be seen and accessed as well.
Success tree display: The success tree display having a hierarchical display and the group-wise display.
Trend display: A trend display showing the actual-expected value trend.
Alarms display: A grid-based alarm display showing the latest alarm on the top display.
Reports: Reports can be generated about the health status and regular alarm.
Configuration Manager: A configuration manager, which invokes at the beginning of the PHI Client application. The configuration manager checks for the port and the servers IP address; if not able to connect, the configuration manager window will pop up at the startup.
The rest is here:
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Predictive Analytics: The Holy Grail in Business Intelligence – Newsweek
In today's world, businesses have a wealth of data at their fingertips. However, data will be of no use to a business if it is not utilized to gain insights and make informed decisions to enhance business operations. Business intelligence, or BI, helps businesses achieve this goal. BI is a technology-driven way of analyzing information and delivering actionable insights that can help managers, executives and end users gain detailed insights that aid them in making decisions. It helps people to assist in making decisions on what they can do for getting insights.
While traditional BI tools primarily monitor historical data and current data, predictive analytics utilizes data, statistical algorithms, data mining methodologies, analytical techniques and machine learning algorithms to determine the likely outcomes based on historical data to provide insights into the future. In addition to being able to determine what has happened in the past and why it happened, predictive analytics also helps you understand what could happen in the future. By identifying opportunities, they allow businesses to be proactive and agile.
For businesses, predictive analysis is crucial. Digital transformations and increased competition have made companies more competitive than ever before. Using predictive analysis is like having a strategic vision of the future, mapping out the opportunities and threats. Therefore, companies should look for predictive models that:
Any industry can use predictive analytics to forecast sales, detect risks, and improve sales operations. Predictive analytics can also be used to detect fraud, evaluate credit risk or find new investment opportunities in a financial institution. Using predictive analytics, manufacturers can identify factors that result in quality reduction, production failures, and distribution risks.
With predictive analytics, sales forecasting can create real value for businesses. Many other business decisions are influenced by accurate sales forecasts. However, sales forecasting is still a time-consuming activity for sales professionals who often rely on Excel spreadsheets and other tools that do not provide sufficient analytics and insights to make accurate sales forecasts. As a result of advanced predictive analytics, sales professionals can automate rolling forecasts and have more transparency and smarter decision support.
Using an ensemble of machine learning algorithms, AI-based forecasting optimizes forecasts. Depending on which business metric you're forecasting, the system selects a model that's uniquely suitable. The process consists of a series of steps:
Regardless of the business model, forecasting is extremely important for businesses as it creates some insurance for future business outcomes. In addition to detecting and mitigating potential issues in advance, it helps organizations make informed decisions and set budgets and business goals. AI helps businesses oversee all these aspects with increased accuracy in the forecasting process.
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Predictive Analytics: The Holy Grail in Business Intelligence - Newsweek
Technical Director Insights: Are We Delivering the Value of DSEA Information? – Society of Petroleum Engineers
Silviu Livescu, the technical director for SPEs Digital Science and Engineering Analytics (DSEA) discipline, asked me whether the unprecedented ability to identify underperforming wells and the rebound in wellhead oil and gas prices has led to a boom in well remediation activities (e.g., well cleanouts, reperforation, acidization, refracturing, and sidetracking).
To be honest, I dont know. But I dont think remediation activities have increased much over the prepandemic levels.
Rehabilitating underperforming or inactive wells, or strings, should be the normal response to relatively good product prices. The methodologies will vary from one operator to another, with the capabilities of the local service providers or the mobilization costs for state-of-the-art tools.
I have always been fascinated by the intellectual challenges around skin damage identification and business opportunities that cost-effective, low-risk remediation techniques represent for adding incremental asset value.
The insidious deterioration in inflow performance is often masked or complicated by other reservoir-related processes. Moreover, while the asset is contract-, facility- or equipment-limited, there is only a weak business case to maximize the inflow performance relationship or injectivity indices.
Moreover, there may be better, low-risk, modest-cost opportunities to optimize the production system using an integrated production model (IPM) or modern process controls. Nevertheless, the process of building the IPM will often result in an inventory of wells for which performance is deemed to be low or off. This inventory will include opportunities to add value by extending the plateau or accelerating resource recovery. Moreover, there is always a small element of capture in having the wells decline more acutely into the economic limit (Fig. 1).
DSEA algorithms offer a great way of ranking these value-creation opportunities and sequencing well-servicing campaigns. The ranking criteria will depend on the decision metrics used by the specific operators and their joint-venture partners and, possibly, based on the terms in the production-sharing agreement. This is a good reason for production and facilities generalists to keep abreast with modern decision analysis technologies via the SPE Management Technical Section.
My first inclination has always been to check for fill in underperforming producers and injectors with modest well inclinations. Accumulated fill can result in partial penetration skin or a reduction in the effectively drained permeability thickness (kh). Over my career, I have seen significant uplift from simple wellbore and perforation or sand-control device cleaning operations.
However, holdup depth measurements are quite an expensive exercise on high-angle or near-horizontal wells. Where equipment and crew mobilization costs dominate, problem investigation and diagnosis plans need to include a decision tree on what else should be done while the coiled tubing or downhole tractor is still on location, potentially including the entire first-stage remediation program.
Nevertheless, some of the dumbest decisions that I have made related to not putting enough thought into the design of solids, gunk, or scale cleanout operations include:
I suspect that there are now machine-learning-driven remediation program design tools to help the service company personnel avoid many of these stumbling blocks.
Moreover, we know that problems can be minimized by the operator funding a multifunctional exercise to run through the jobs on paper and conduct a hazard identification or risk mitigation exercise with the service company personnel and in-field job supervisors. (Obviously, this is even more effective if it builds on the lessons captured during the last campaign.) The expression an ounce of prevention is worth a pound of cure dates to the 13th century and was popularized by Benjamin Franklin in the 1700s.
Automated decline cure or rate-time analysis and integrated production performance models (e.g., PERFORM, PIPESIM, PROSPER, SAM, Wellflo, Wellflow, and WEM) can help identify these well-performance problems and the uplift opportunities. VOI analysis also can be preprogrammed and used to determine if a single or multirate test or production-logging operation will improve the probability of success or identifying the optimal pathway to the optimal remediation program.
The well-performance analysis tools can then be tuned to the actual flowing bottomhole pressures at the tested rates.
Fig. 2Simulating the effects of total effective skin (S = 5, 3, 0, and + 5) on the performance of a 10,000-ft total vertical depth gas well with 2.875-in. outside diameter tubing at two water/gas ratios (1 and 5 bbl/MMscf) producing against a backpressure of 500 psi.
The well shown in Fig. 2 had tested at 425 Mscf/D at 500 psi wellhead pressure with a total skin of +5 and a P skin of >1,000 psi. The reservoir management team (RMT) had expected a modest negative skin from this completion. This information begs lots of questions and seeds a brainstorming session by the RMT.
Note that the inflow performance ratios for this well are so steep that incremental compression will currently add only a modest uplift at the current reservoir pressures.
A remediation project should follow the normal project management process by weighing up the available technical options in Stage 2 (Fig. 3). This review needs to include the HSE, technical and commercial risks and evaluate the available risk-management options.
IHRDC
It is also important to recognize that the RMT may have set constraints on the maximum effective drawdown beyond the damaged zone, the optimal producing gas/oil ratios or water/oil ratios at any given cumulative production volume, or pattern voidage replacement ratios.
As Amir Al-Wazzan, production assurance manager for technology deployment and innovation at Dragon Oil, pointed out to me, the decision on whether to select a conventional or cutting-edge remediation technology will depend on the resources available from the local service providers and the costs and perceived risks involved in mobilizing, deploying, and using the latest technologies. In many cases, it is highly attractive to use familiar, proven technologies to clean out, revitalize, and re-equip idle wells or underperforming wells. Workovers may offer greater upside, but the options may be limited by the available budget and the perceived HSE or well integrity risks.
Pilot testing of new technologies needs to be benchmarked against providing an equal effort into optimizing and supervising well-established approaches for well remediation in a specific asset and region. Nevertheless, as Livescu suggested, data mining and modern data analysis techniques allow us to do a much better job in finding good field analogies where new approaches to well remediation and restimulation have proven to have a high probability of commercial success.
Dan Hill, professor at the College of Engineering at Texas A&M, said in the promotional SPE Live for his 20222023 Distinguished Lecture Acid Stimulation of Carbonate Formations: Matrix Acidizing or Acid Fracturing? that most carbonates are so acid soluble that achieving a negative skin should be relatively easy. His talk presents a relatively simple methodology for evaluating whether to attempt an acid fracture stimulation or a complex wormhole configuration with a well-executed matrix acid treatment. This decision depends on the rock properties and closure stress, which will vary with depth and with reservoir pressure decline.
I am eager for the opportunity to see the entire presentation and ask questions about
In Schlumbergers SPE Tech Talk New Single-Stage Sandstone Acidizing SolutionHigh Efficiency and Low Risk, Pedro Artola and Temi Yusuf pointed out that this proprietary one-step acid blend provides a low-risk, low-volume, cost-effective method to stimulate sandstone reservoirs containing less than 20% carbonates. The blend is specifically designed to avoid the overspending challenges with conventional low-strength, mud acid treatments at temperatures of up to 150C. A single fluid also helps reduce treatment complexity in multistage treatments in treating zones with more than 6 m of net pay, where diversion is required.
I have also found it quite challenging to identify the most cost-effective options for treating underperforming or damaged water-injection and saltwater disposal wells. The root causes on injection-well damage will generally depend on the water-plant and wellhead filtration strategy, the nature of any water treatment deficiencies, the nature and frequency of process upsets, and the corrosion or fluid-scaling tendencies that often vary over the life of the asset.
In well-established injection wells, this likely involves damage to the thermally induced fractures, rather than being restricted to the perforation tunnels. With thick pay zones, this poses significant treatment diversion challenges. Moreover, in high-angle or near-horizontal injection wells, there will likely be multiple, near-parallel fractures, only a few of which will control the well performance.
In any event, it is often necessary to conduct at least part of the treatment close to the pressure limits of the completion or wellhead and, potentially, at the fracture propagation pressure.
The SPE Production and Facilities Advisory Committee have been discussing the need for additional, vertically integrated, specialized events on well remediation to disseminate the latest experience and experience in pilot testing of new technologies. However, it is possible that these topics are adequately covered by presentations at SPE regional workshops or the larger sections (such as Saudi, Gulf Coast, Aberdeen/Stavanger, and Brazil) and at cross-functional events such as SPE's Annual Technical Conference and Exhibition, the International Petroleum Technology Conference, regional Offshore Technology Conference events, and Offshore Europe.
To avoid overlap in this content and competition for sponsorship dollars and quality papers and presentations, I have started a discussion chain on SPE Connect to solicit your input.
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I had an easier time during 2008: TikToker says theyve applied to 300 tech jobs and gotten 3 interviews – The Daily Dot
A TikToker said he applied to over 300 jobs but only received three interviews in the past two years, despite having 15 years of experience in his field, according to a video.
The TikToker, @hksmash, said he has taken all the Courseraan online certification platformcertifications for his career just for shits and gigs and has still only had three interviews, two of which he was ghosted on by the hiring manager.
The one interview I had this year, I actually got a job offer, he said in the video. Only for that offer to be rescinded because they no longer had budget for the role.
His post came in response to another TikToker saying that no one seemed to be having any luck getting hired right now.
In the comments, hksmash clarified he works in tech and IT support.
According to the U.S. Bureau of Labor Statistics, 5.9 million people were not in the labor force but wanted a job in July 2022, despite the narrative that there is a labor shortage. There were 10.7 million job openings during the month of June.
I had an easier time finding a job during the 2008 housing market crash, he added.
TikTok users in the comments vented about their frustration with the job market.
Ive heard the theory that its data mining, one commentor said. they say theyre hiring, when theyre actually not, to get data from the applications.
Had a feeling this was a tech thing, another wrote. Most companies are in hiring freezes or downsizing.
Bro, i just got rejected for an internal position for a teachable position because I dont have enough experience, another said.
*First Published: Aug 25, 2022, 3:32 pm CDT
Jacob Seitz is a freelance journalist originally from Columbus, Ohio, interested in the intersection of culture and politics.
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A comprehensive meta-analysis and prioritization study to identify vitiligo associated coding and non-coding SNV candidates using web-based…
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Nearly 3 Years Later, SolarWinds CISO Shares 3 Lessons From the Infamous Attack – DARKReading
On Dec. 8, 2020, FireEye announced the discovery of a breach in the SolarWinds Orion software while it investigated a nation-state attack on its Red Team toolkit. Five days later, on Dec. 13, 2020, SolarWinds posted on Twitter, asking "all customers to upgrade immediately to Orion Platform version 2020.2.1 HF 1 to address a security vulnerability." It was clear: SolarWinds the Texas-based company that builds software for managing and protecting networks, systems, and IT infrastructure had been hacked.
More worrisome was the fact that the attackers, which US authorities have now linked to Russian intelligence, had found the backdoor through which they infiltrated the company's system about 14 months before the hack was announced. The SolarWinds hack is now almost 3 years old, but its aftereffects continue to reverberate across the security world.
Let's face it: The enterprise is constantly under threat either from malicious actors who attack for financial gains or hardened cybercriminals who extract and weaponize data crown jewels in nation-state attacks. However, supply chain attacks are becoming more common today, as threat actors continue to exploit third-party systems and agents to target organizations and break through their security guardrails. Gartner predicts that by 2025, "45% of organizations worldwide will have experienced attacks on their software supply chains," a prediction that has created a ripple across the cybersecurity world and led more companies to start prioritizing digital supply chain risk management.
While this is the right direction for enterprises, the question still lingers: What lessons have organizations learned from a cyberattack that went across the aisle to take out large corporations and key government agencies with far-reaching consequences even in countries beyond the United States?
To better understand what happened with the attack and how organizations can prepare for eventualities like the SolarWinds hack, Dark Reading connected with SolarWinds CISO Tim Brown for a deeper dive into the incident and lessons learned three years on.
Brown admits that the very name SolarWinds serves as a reminder for others to do better, fix vulnerabilities, and strengthen their entire security architecture. Knowing that all systems are vulnerable, collaboration is an integral part of the cybersecurity effort.
"If you look at the supply chain conversations that have come up, they're now focusing on the regulations we should be putting in place and how public and private actors can better collaborate to stall adversaries," he says. "Our incident shows the research community could come together because there's so much going on there."
After standing at the frontlines of perhaps the biggest security breach in recent years, Brown understands that collaboration is critical to all cybersecurity efforts.
"A lot of conversations have been ongoing around trust between individuals, government, and others," he says. "Our adversaries share information and we need to do the same."
No organization is 100% secure 100% of the time, as the SolarWinds incident demonstrated. To bolster security and defend their perimeters, Brown advises organizations to adopt a new approach that sees the CISO role move beyond being a business partner to becoming a risk officer. The CISO must measure risk in a way that's "honest, trustworthy, and open" and be able to talk about the risks they face and how they are compensating for them.
Organizations can become more proactive and defeat traps before they are sprung by using artificial intelligence (AI), machine learning (ML), and data mining, Brown explains. However, while organizations can leverage AI to automate detection, Brown warns there's a need to properly contextualize AI.
"Some of the projects out there are failing because they are trying to be too big," he says. "They're trying to go without context and aren't asking the right questions: What are we doing manually and how can we do it better? Rather, they're saying, 'Oh, we could do all of that with the data' and it's not what you necessarily need."
Leaders must understand the details of the problem, what outcome they are hoping for, and see if they can prove it right, according to Brown.
"We just have to get to that point where we can utilize the models on the right day to get us somewhere we haven't been before," he says.
IT leaders must stay a step ahead of adversaries. However, it's not all doom and gloom. The SolarWinds hack was a catalyst for so much great work happening across the cybersecurity board, Brown says.
"There are many applications being built in the supply chain right now that can keep a catalog of all your assets so that if a vulnerability occurs in a part of the building block, you will know, enabling you to assess if you were impacted or not," he says.
This awareness, Brown adds, can help in building a system that tends toward perfection, where organizations can identify vulnerabilities faster and deal with them decisively before malicious actors can exploit them. It's also an important metric as enterprises edge closer to the zero-trust maturity model prescribed by the Cybersecurity and Infrastructure Security Agency (CISA).
Brown says he is hopeful these lessons from the SolarWinds hack will aid enterprise leaders in their quest to secure their pipelines and remain battle-ready in the ever-evolving cybersecurity war.
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Nearly 3 Years Later, SolarWinds CISO Shares 3 Lessons From the Infamous Attack - DARKReading
‘A Historic Moment’: New Guidance Requires Federally Funded Research to Be Open Access – The Chronicle of Higher Education
In a move hailed by open-access advocates, the White House on Thursday released guidance dictating that federally funded research be made freely and immediately available to the public.
The Office of Science and Technology Policys guidance calls for federal agencies to make taxpayer-supported research publicly available immediately, doing away with an optional 12-month embargo. It also requires the data underlying that research to be published. Federal agencies have until December 31, 2025, to institute the guidance.
The American people fund tens of billions of dollars of cutting-edge research annually. There should be no delay or barrier between the American public and the returns on their investments in research, Alondra Nelson, head of the office, known as OSTP, said in a news release.
Heather Joseph, executive director of the Scholarly Publishing and Academic Resources Coalition, told The Chronicle that the announcement was extremely welcome news. The provision requiring data to be published, she said, is especially significant and will help boost scientific integrity and trust in science by allowing other scientists to validate researchers conclusions.
Nelsons memo outlining the guidance cites the Covid-19 pandemic as a powerful case study on the benefits of delivering research results and data rapidly to the people. At the outset of the pandemic, scholarly publishers lifted their paywalls for Covid-related articles and made research available in machine-readable formats, which Joseph said allowed scholars to use text- and data-mining, artificial-intelligence, and computational techniques on others work.
The new guidance expands on a 2013 memo issued by OSTP during the Obama administration. That memo applied only to federal agencies that fund more than $100 million in extramural research; the Biden memo has no such cap. That means that, for example, work funded by the National Endowment for the Humanities, which didnt meet the $100-million threshold in 2013, will for the first time be covered by federal open-access policy, Peter Suber, director of the Harvard Open Access Project, wrote on Twitter.
The Association of Research Libraries welcomed the expansion in a statement that described the memo as a historic moment for scientific communications.
Lifting the yearlong embargo that some journals have imposed on papers they publish will promote more equitable access to research, some said. The previous policy limited immediate equitable access of federally funded research results to only those able to pay for it or have privileged access through libraries or other institutions, two officials in the White House office wrote in a blog post. Financial means and privileged access must never be the prerequisite to realizing the benefits of federally funded research that all Americans deserve.
Thats a theme President Biden has championed for years. Thursdays White House news release quoted his remarks to the American Association for Cancer Research as vice president in 2016, when he criticized taxpayer-funded research that sits behind walls put up by journals subscription fees.
Sen. Ron Wyden, a Democrat from Oregon, released a statement praising the guidance for unlocking federally funded research from expensive, exclusive journals and calling it an astronomical win for innovation and scientific progress. (Wyden and a fellow Democratic senator, Ed Markey of Massachusetts, in February urged Nelson to establish an open-access policy.) And Michael Eisen, a co-founder of the open-access project PLOS, applauded the guidance on Twitter. The best thing I can say about this new policy, he wrote, is that publishers will hate it.
Its not clear how academic publishers, whose profits and business model will be affected, plan to adapt to the new guidelines. A spokesperson for Elsevier, a leading commercial publisher of academic journals, wrote in an email to The Chronicle that Elsevier actively supports open access to research and that 600 of its 2,700 journals are fully open-access (nearly all of the others, the spokesperson wrote, enable open-access publishing). We look forward to working with the research community and OSTP to understand its guidance in more detail.
Emails from The Chronicle to three other major academic publishers Springer Nature, Taylor & Francis, and Wiley did not draw an immediate response.
Some commentators worried that publishers would raise the article-processing charges, or APCs, associated with open-access publishing in their journals. But Joseph, of the academic-resources coalition, said she hopes language in the guidance that encourages measures to reduce inequities in publishing, particularly among early-career scholars and those from underserved backgrounds, will prevent that.
Those publishers that try to charge ridiculously high APCs will find it difficult, because inequity in publishing means Im priced out of being able to publish. I cant afford to contribute my research article to the scientific record, Joseph said. The White Houses blog post also noted that it was working to ensure support for more vulnerable members of the research ecosystem unable to pay rising costs associated with publishing open-access articles.
And authors have other options by which to make their work open, Joseph said. The guidance, she noted, allows authors to make their manuscripts freely available in an agency-designated repository even if its also published in a journal.
The National Institutes of Health, which finances more than $32 billion a year in biomedical research, promised on Thursday to comply with the new guidance. We are enthusiastic to move forward on these important efforts to make research results more accessible, and look forward to working together to strengthen our shared responsibility in making federally funded research results accessible to the public, Lawrence A. Tabak, acting director of the NIH, wrote in a statement.
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Data classification: What it is and why you need it – ComputerWeekly.com
CIOs and IT directors working on any project that involves data in any way are always more likely to succeed when the organisation has a clear view of the data it holds.
Increasingly, organisations are using data classification to track information based on its sensitivity and confidentiality, as well as its importance to the business.
Data that is critical to operations or that needs to be safeguarded such as customer records or intellectual property is more likely to be encrypted, to have access controls applied, and be hosted on the most robust storage systems with the highest levels of redundancy.
AWS, for example, defines data classification as a way to categorise organisational data based on criticality and sensitivity in order to help you determine appropriate protection and retention controls.
However, data protection measures can be costly, in cash terms and potentially in making workflows more complex. Not all data is equal, and few firms have bottomless IT budgets when it comes to data protection.
But a clear data classification policy should ensure compliance and optimise costs and it can also help organisations make more effective use of their data.
Data classification policies are one of the Swiss Army knives of the IT toolbox.
Organisations use their policies as part of their business continuity and disaster recovery planning, including setting backup priorities.
They use them to ensure compliance with regulations such as GDPR, PCI-DSS and HIIPA.
These policies are fundamental to effective data security, setting rules for encryption, data access, and even who can amend or delete information.
Data classification policies are also a key part of controlling IT costs, through storage planning and optimisation. This is increasingly important, as organisations store their data in the public cloud with its consumption-based pricing models.
But it is also essential to match the right storage technologies to the right data, from high-performance flash storage for transactional databases, to tape for long-term archiving. Without this, firms cannot match storage performance, associated compute and networking costs, to data criticality.
In fact, with organisations looking to drive more value from their information, data classification has another role helping to build data mining and analytics capabilities.
The topic of data management has crept up in importance among the leadership teams of many organisations over the past few years, says Alastair McAulay, an IT strategy expert at PA Consulting.
There are two big drivers for this. The first driver is a positive one, where organisations are keen to maximise the value of their data, to liberate it from individual systems and place it where it can be accessed by analytics tools to create insight, to improve businesses performance.
The second driver is a negative one, where organisations discover how valuable their data is to other parties.
Organisations need to protect their data, not just against exfiltration by malicious hackers, but against ransomware attacks, intellectual property theft and even the misuse of data by otherwise-trusted third parties. As McAulay cautions, firms cannot control this unless they have a robust system for labeling and tracking data.
Effective data classification policies start out with the three basic principles of data management:
This CIA model or triad is most often associated with data security, but it is also a useful starting point for data classification.
Confidentiality covers security and access controls ensuring only the right people view data and measures such as data loss prevention.
Integrity ensures that data can be trusted during its lifecycle. This includes backups, secondary copies and volumes derived from the original data, such as by a business intelligence application.
Availability includes hardware and software measures such as business continuity and backup and recovery, as well as system uptime and even ease of access to the data for authorised users.
CIOs and chief data officers will then want to extend these CIA principles to fit the specific needs of their organisations and the data they hold.
This will include more granular information on who should be able to view or amend data, extending to which applications can access it, for example through application programming interfaces (APIs). But data classification will also set out how long the data should be retained for, where it should be stored, in terms of storage systems, how often it should be backed up, and when it should be archived.
A good data backup policy may well rely on a data map so that all data used by the organisation is located and identified and therefore included in the relevant backup process, says Stephen Young, director at data protection supplier AssureStor. If disaster strikes, not everything can be restored at once.
One of the more obvious data classification examples is where organisations hold sensitive government information. This data will have protective markings in the UK, this ranges from official to top secret which can be followed by data management and data protection tools.
Firms might want to emulate this by creating their own classifications, for example by separating out financial or health data that has to comply with specific industry regulations.
Or firms might want to create tiers of data based on their confidentiality, around R&D or financial deals, or how important it is to critical systems and business processes. Unless organisations have the classification policy in place, they will not be able to create rules to deal with the data in the most appropriate way.
A good data classification policy paves the way for improvements to efficiency, quality of service and greater customer retention if it is used effectively, says Fredrik Forslund, vice-president international at data protection firm Blancco.
A robust policy also helps organisations to deploy tools that take much of the overhead out of data lifecycle management and compliance. Amazon Macie, for example, uses machine learning and pattern matching to scan data stores for sensitive information. Meanwhile, Microsoft has an increasingly comprehensive set of labelling and classification tools across Azure and Microsoft 365.
However, when it comes to data classification, the tools are only as good as the policies that drive them. With boards increasing sensitivity to data and IT-related risks, organisations should look at the risks associated with the data they hold, including the risks posed by data leaks, theft or ransomware.
These risks are not static. They will evolve over time. As a result, data classification policies also need to be flexible. But a properly designed policy will help with compliance, and with costs.
There is no avoiding the fact that creating a data classification policy can be time-consuming, and it requires technical expertise from areas including IT security, storage management and business continuity. It also needs input from the business to classify data, and ensure legal and regulatory compliance.
But, as experts working in the field say, a policy is needed to ensure security and control costs, and to enable more effective use of data in business planning and management.
Data classification helps organisations reduce risk and enhance the overall compliance and security posture, says Stefan Voss, a vice-president at IT management tool company N-able. It also helps with cost containment and profitability due to reduction of storage costs and greater billing transparency.
Also, data classification is a cornerstone of other policies, such as data lifecycle management. And it helps IT managers create effective recovery time objectives (RTOs) and recovery point objectives (RPOs) for their backup and disaster recovery plans.
Ultimately, organisations can only be effective in managing their data if they know what they have, and where it is. As PA Consultings McAulay says: Tools will only ever be as effective as the data classification that underpins them.
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Data classification: What it is and why you need it - ComputerWeekly.com