mud pump maintenance training pricelist

Graduates of the program have found that their newly improved maintenance skills have extended the life of the fluid ends on their site. Beyond increasing lifespan, customers have also seen benefits in the form of lower repair costs, decreased production costs and a reduction in workplace hazards.

Pump University’s knowledgeable instructors teach customers valuable maintenance tips through workbooks, visual presentations and hands-on training. Each three hour class can accommodate 10 to 15 employees. The classes are designed for drilling hands, but engineers and other employees have found value in the courses. Many participants find that these courses teach them about expendables as well as fluid ends.

Pump University participants receive a certificate upon completion of the program. The certification verifies Pump U graduates are equipped with the most up-to-date knowledge of fluid end maintenance practices.

Pump University is just one part of GD Energy Products’ continually growing service options. GD Energy Products makes the most durable pumps and fluid ends in the industry, and proper maintenance training ensures customers can maximize the life of their GD Energy Products products while minimizing costly downtime.

Titan Oil Tools offers high quality mud pump expendables and mud pump replacement parts for the wide variety of mud pumps, centrifugal pumps found on the world market today. These quality pump parts offer great performance and our pricing will save you money.

We are your oilfield supplier of choice for mud pump spares and mud pump parts: mud pump piston liners and pistons. All mud pump parts can be sourced for fast delivery; Come to us for mud pump liners, pistons, piston rods and parts, pony rods, threaded rings and caps,and more. Try us for duplex and triples pump spares and duplex and triplex mud pump parts, and also valve parts like valve seats. gland nut, and mud pump gaskets.

Mud pump liners may come in chrome, alumina ceramic and zirconia ceramic. Chrome liners and alumina ceramic liners are less costly, their cost of replacement over one year as the chart below shows, is much more than zirconia ceramic liners.

The table below shows a Mud Pump Liner Cost of Ownership which shows a reasonable cost comparison for a rig in continuous service for 36 months. This of course does not include the high cost of maintenance downtime and the cost of labor.

The prices below are based on cost/cylinder. Savings increase when you add up the number of pump cylinders on your rigs and extend the savings to understand the big difference it can make for your budget.

Maintaining /monitoring the mud pumps and mud pits during operations. Notifying the Driller immediately of any unexplained pit level increases/decreases Verify and record drilling fluid properties at ...

Perform daily inspections and maintenance on electrical equipment. Comply with the permit to work and isolation systems. Carry out periodic maintenance according to the guidelines Maintain, troubleshoot ...

Assist internal and external customers with troubleshooting of Rig Equipment (Top Drive, Catwalk, Wrench, Drawworks, VFDs, Generators, Mud Pumps, BOP) Expeditiously work to troubleshoot and resolve downtime ...

Assist higher level technicians, as well as work independently with minimal supervision. Promote a safe and quality environment in which to work Perform preventative maintenance requirements and maintain ...

Pumps of various designs and applications are encountered nowadays throughout petrochemical and process industries as well as in power generation and environmental engineering. Pumping systems have to provide efficient transport of various liquids, sometimes complex and difficult two-phase fluids. Although there has been great progress in pump design in recent years, there are still many issues related to the mechanical and hydraulic problems to be considered that are associated with efficient operation of the whole pumping system. There is always need for optimization of the pumping system in operation with the goal of reducing the overall costs of production.

This intensive training course will provide an overview of important technical aspects of pumps and pumping systems as encountered in industrial applications. Of special interest will be centrifugal pumps as well as other types of positive displacement pumps. This PetroKnowledge course will also concentrate on the thorough understanding of technical principles of fluid flow in pumping systems as a prerequisite for successful pump operation. The focus will be on the elimination of potential problems as well as on pump inspection and condition monitoring. The training course will include several real-life case studies that will be solved in the form of workshops in order to illustrate the methods of efficient operation and maintenance of pumping systems.

Uses the real-life example of a highway system to introduce valves and manifolds on a drilling rig. Walks through the definition, function, and importance of the pump, standpipe, and choke manifolds through vibrant animations. Introduces the adjustable choke and briefly goes into its importance in well control operations.

Introduces friction and examines how frictional losses act against any movement along a surface. Identifies Pump Pressure as the pressure needed to overcome the frictional losses throughout the entire system. Lastly, explains how Annular Friction Loss can contribute to bottomhole pressure.

Explains the importance of fluid measurement in detecting problems in the wellbore. Introduces the Pit Level Indicator, used to measure the amount of fluid returning to the mud tanks, the Mud Return Indicator, used to measure the speed of fluid returning to the mud tanks, and the Mud Pump Stroke counter, used to count how many strokes of fluid have been pumped into the well.

Explains the three most important parameters that need to be recorded after shut-in: Shut in Tubing Pressure (SITP), Shut in Casing Pressure (SICP), and Estimated Pit Gain. Walks through the importance of SITP and SICP in understanding Formation Pressure and explains why SITP is generally lower than SICP. Also introduces the procedure involved in recording slow pump rates or slow circulating rates.

Terminology; Calculating total head, horsepower, and available vs required NPSH; Pump performance; Head capacity; Efficiency; Horsepower curves; Pump selection

When industrial equipment breaks, the resulting problem often is not the cost of replacing that equipment but the forced downtime. A production line standing still may mean thousands of dollars lost every minute. Performing regular maintenance can help avoid unplanned downtime, but it does not guarantee that equipment will not fail.

What if the machine could indicate when one of its parts was about to fail? What if the machine could even tell you which part needed to be replaced? Unplanned downtime would be reduced considerably. Planned maintenance would be performed only when necessary rather than at fixed intervals. This is the goal of predictive maintenance: avoiding downtime by using sensor data to predict when maintenance is necessary.

At the heart of developing any predictive maintenance algorithm is sensor data, which can be used to train a classification algorithm for fault detection. Meaningful features are extracted from this data in a preprocessing step and used to train a machine learning algorithm for predictive maintenance. This algorithm is exported to simulation software such as Simulink® for verification and then deployed as code to the control unit of the machine.

A solution to this challenge is to create a digital twin of the equipment and generate sensor data for various fault conditions through simulation. This approach enables engineers to generate all sensor data needed for a predictive maintenance workflow, including tests with all possible fault combinations and faults of varying severity.

This article discusses the design of a predictive maintenance algorithm for a triplex pump using MATLAB®, Simulink, and Simscape™ (Figure 1). A digital twin of the actual pump is created in Simscape and tuned to match measured data, and machine learning is used to create the predictive maintenance algorithm. The algorithm needs only the outlet pump pressure to recognize which components or combinations of components are about to fail.

A triplex pump has three plungers driven by a crankshaft (Figure 2). The plungers are laid out so that one chamber is always discharging, making the flow smoother, reducing pressure variation, and thereby lowering material strain as compared with a single-piston pump. Typical failure conditions of such a pump are worn crankshaft bearings, leaking plunger seals, and blocked inlets.

CAD models for pumps, which are often available from the manufacturer, can be imported into Simulink and used to build a mechanical model of the pump for 3D multibody simulation. To model the dynamic behavior of the system, the pump now needs to be complemented by the hydraulic and electric elements.

The plot in Figure 3 shows that simulating the pump with rough estimates (blue line) does not sufficiently match the field data (black line). The blue line resembles the measured curve to some extent, but the differences are obviously great.

In the simulation of the pump shown at the top of Figure 6, two faults have been enabled: a blocked inlet and a seal leakage at plunger 3. These faults are indicated by the red circles. The plot in Figure 6 shows the simulation results for outlet pressure both as a continuous line (blue) and sampled with noise (yellow). The data generated by the simulation must include quantization effect noise because we need to train our fault detection algorithm with data that is as realistic as possible.

Figure 6. Top: Pump schematic showing the blocked inlet and seal leakage. Bottom: Plot of the outlet pressure simulation (blue line) and sampled with noise (yellow line).

The green box in Figure 6 indicates the normal value range for outlet pressure. There are spikes clearly leaving the normal range, indicating some fault. This plot alone would tell an engineer or operator that something is wrong with the pump, but it is still impossible to judge exactly what the fault is.

We use this simulation to generate pressure data for the pump under all possible combinations of fault conditions. Approximately 200 scenarios were created for the digital twin. Each scenario must be simulated numerous times to account for quantization effects in the sensor. Since this approach requires several thousand simulations, we want to be able to speed up the data generation process.

Another approach is to use the Fast Restart feature in Simulink, which takes advantage of the fact that many systems require a certain settling time until a steady state is achieved. With Fast Restart, this portion of the test needs to be simulated only once. All subsequent simulations will start from the point where the system has reached steady state. In the current example, the settling time would make up about 70% of the simulation time required for a single test (Figure 7). Consequently, about two-thirds of the simulation time can be saved using Fast Restart. Since Fast Restart can be configured from the MATLAB command line and from scripts, it is perfectly suited to automating the training process.

The next step is to use the simulation results to extract training data for the machine learning algorithm. Predictive Maintenance Toolbox™ provides various options for extracting training data. Since the signal we are looking at here is a periodic one, a fast Fourier transform (FFT) appears most promising. As shown in Figure 8, the result is a small number of clearly separated spikes of different magnitudes for individual faults as well as for fault combinations. This is the kind of data that a machine learning algorithm can handle very well.

Now that all the data required for training a fault detection algorithm is available, it can be imported into Statistics and Machine Learning Toolbox™. We will use a subset of the generated data to verify the trained algorithm.

We visualize the results of the training process in Statistics and Machine Learning Toolbox. These visualizations enable us to compare the strengths and weaknesses of different algorithms and determine whether additional training data is needed. We select the trained algorithm that achieved the highest accuracy for determining the pump fault from the measured data. We import that algorithm into the digital twin for verification using seven test cases saved for this purpose (Figure 9). As the final results show, the classification algorithm is able to detect all seven scenarios securely. It is now ready for deployment on the control unit.

A real-world application of this workflow is industrial equipment that will be used across the world under widely divergent environmental conditions. Such equipment may be subject to change: A new seal or valve supplier may be selected, or the pump may be operated with various kinds of fluids or in new environments with different daily temperature ranges. All these factors affect the pressure measured by the sensor, possibly making the fault detection algorithm unreliable or even useless. The ability to quickly update the algorithm to account for new conditions is critical for using this equipment in new markets.

The workflow described here can be automated using scripts in MATLAB, and most of the work can be reused. The only step that needs to be repeated is data acquisition under conditions comparable to those the pump will face in the field.

With the latest advances in smart interconnectivity, it will even be possible for machine makers to deliver equipment to customers with provisional settings, remotely collect data under actual onsite conditions, train the fault detection algorithm, and then remotely redeploy it to the machine. This will open up new customer support opportunities, including the retraining of fault detection on equipment that has been in use for some time under site-specific conditions. The insights gathered on numerous machines will benefit both customers and manufacturers.

Predictive maintenance helps engineers determine exactly when equipment needs maintenance. It reduces downtime and prevents equipment failure by enabling maintenance to be scheduled based on actual need rather than a predetermined schedule. Often it is too costly or even impossible to create the fault conditions necessary for training a predictive maintenance algorithm on the actual machine. A solution to this challenge is to use field data from the fully working machine to tune a physical 3D model and create a digital twin. The digital twin can then be used to design a predictive maintenance detection algorithm for deployment to the controller of the actual equipment. The process can be automated, enabling quick adjustment to varying conditions, materials handled, and equipment configurations.