Maximize Fracking Profitability with ANSYS

This article explains how ANSYS and few other tools can be used to perform hydraulic fracturing, or commonly known as fracking, to reduce costs and increase profitability of shale gas projects.

Shale Gas

Shale gas is a form of natural gas trapped within shale formations. Because of its abundance, shale gas is a lot cheaper than it has been in years. Hydraulic fracturing or fracking helps in extracting it efficiently.

According to American Enterprise Institute, “the direct benefit of increasing oil and gas production includes the value of increased production attributable to the technology. In 2011, the USA produced 8.5 trillion cubic feet of natural gas from shale gas wells. Taking an average price of $4.24 per thousand cubic feet, that’s a value of about $36 billion, due to shale gas alone.” As a result of increase in fracking, natural gas imports in United States reduced by 25 percent between 2007 and 2011.

What is Fracking?

The term simply means creating fractures using hydraulic fluids. In this technique, production teams pump huge volumes of water and proppant at high pressure into the gas well. They also mix a few chemicals, which improve fracking performance, along with the water during pumping. Shale layers, being less permeable, minimize the flow of the natural shale gas trapped.

Fracking is useful in creating a connected fractured network between pores of the rock through which natural gas escapes out. In the first step, production teams drill horizontally along the shale layers. From the perforations, specialists pumps water into the rock. Since water is sent in with high pressures, the shale layers fractures. Once the pressure is decreased, they retrieve water from the shale layers leaving behind sand particles. However, the proppant dwells in the rock layers keeping the cracks open thereby allowing gas to escape.

Benefits and Disadvantages of Fracking

Fracking helps in accessing the natural shale gas trapped deep down beneath the earth. With traditional methods of extraction, we cannot exploit this energy potential. Recently-developed methods of vertical and horizontal drilling added favor to fracking. They permit drilling thousands of feet deep inside the ground in order to access the trapped shale gas.

It is said that shale gas causes lesser air pollution when compared to other dirty fuels like coal and oil. However there are ways in which fracking itself can cause more devastating effects such as air emissions and climate change, high water consumption, water contamination, land use, risk of earthquakes, noise pollution, and health effects on humans.

Economic Benefits of Simulation

To achieve an optimal design for a gas well, standard industry practice is to conduct a large number of field trials that require high capital investment and time which significantly increases the project value.

In order to obtain a profitable production of shale gas, I recommend you to use a fully coupled 3D hydraulic-mechanical simulation. Obviously the costs of such simulation are a lot lower than traditional methods. Many of our customers in the Oil & Gas industry have yielded better output with a higher project profitability.

You can find the schematic view of simulating Hydraulic Fracture below.Schematic view for fracking simulation

Essential Pre-Requisites for Simulation

We gather the input data for simulation from different physics such as geology, petrophysics and geomechanics. From the geology of the rock structure, we extract the lithology and layering, altitudes of beddings and natural fracture data. Accurate determination of petrophysical properties for both the reservoir and fluid contents is necessary. We also need to consider features like porosity, permeability and saturation for the reservoir. It also includes evaluating the properties that help in determining the hydrocarbon concentrations in the reservoir and its ability to produce the gas.

Along with the surface and sub-surface properties of the rock, the in-situ stress parameters also have same importance in simulation. I also account for elastic properties and strength parameters of intact rocks. The geomechanical studies of the rock structure also reveal the strength parameters of natural fractures, if any. Using multiPlas, I model these rock-specific material parameters and joints.

Of course, gathering this data can look daunting to you. However our expertise combined with strengths from Dynardo GmbH – the leading global experts in simulation of hydraulic fracturing – can help!

Fracking Simulation – Readying the Model

In the simulation of fracking process, I use a sequential coupled hydraulic-mechanical modeling approach. Therefore, I construct two models – a hydraulic flow model and a mechanical model simultaneously.

3D model with different soil layers for fracking simulation
3D model with different soil layers

To account the strength and stress anisotropies of the rock structure, I need to consider a 3D model. These variables help us to constantly monitor the behavior of fracking process. To capture the anisotropic nature of the rocks, you’ll need strength and stress anisotropies of the rock matrix and fracture system.

Sequentially Coupled Hydraulic-Mechanical Analysis in ANSYS

In ANSYS Mechanical, we start with a transient hydraulic flow analysis (analogous to transient thermal analysis) to understand the pore pressure field. The pressure increases in the fracture-initiated locations due to the pumping of fluid and low permeability of rock. If the pressure is large enough, the rock starts to fail and fractures open up. As a result, the permeability of the rock structure increases and changes the pressure distribution in the hydraulic flow model. From a mechanical perspective, pressure increase changes the effective stresses within the rock. After every fluid time increment, change in the mechanical forces from pore pressure change will be introduced into the mechanical analysis. The forces on every discretization point of the smeared continuum are computed from the pore pressure gradient.

I setup the coupling inside ANSYS in an explicit manner. Consequently, one iteration cycle is performed for every time step. The time step needs to adequately represent the progress of the fracture growth. At each time step, a transient hydraulic flow analysis starts first. Then the mechanical analysis with the updated pressure field from the hydraulic flow model is conducted. The mechanical analysis results in updated stress, plastic strain fields and hydraulic conductivities. i apply the updated hydraulic conductivities to the hydraulic model in the subsequent time step.

Crack expansion in the model while performing fracking analysis
Crack expansion in the model

In mechanical analysis, the development of fractures is represented by a plastic model in ANSYS. As a result, I cannot directly measure fracture openings and hence I’ll need to calculate it based on the plastic strains.

Model Calibration & Optimization of Fracking Paramaters

Because of large number of statistically-varying and reservoir parameters, the reservoir model needs advanced calibration procedure. At first, I will need to calibrate numerical parameters such as maximum permeability of open joints or energy dissipation at pore pressure frontier.

After calibration of all the parameters, I identify the most important parameters contributing to maximum crack volume using optiSLang software. As you will recognize, maximum crack volume correlates to maximum shale gas output. I validate the behavior of such important parameters and then calibrate the analysis model to the field measurements. I use the calibrated model later in order to optimize the simulated volume and predict the gas production rate of the wells.

Summary & Outlook

Evidently, application of simulation to the fracking process will underline its predictability. Simulation cut downs the costs of field trials, brings down the time-to-market thereby significantly increases the project profitability.

If you’re into gas exploration, you should contact us filling this form or by writing to sales@cadfem.in. We’ll be glad to explain some of our recent projects that have benefited customers in Oil & Gas industry.

Share this on:

Demystifying Modal Analysis (Part I)

In this article, I will discuss about modal analysis – a topic that is standard, however I’ll strive to demystify it using a simple example and FAQs.

Motivation for Modal Analysis

As a mechanical engineer, life is always interesting because I can correlate the knowledge gained from books to real life scenarios. As a student, my professor gave a real example of a bridge failure due to marching soldiers. What followed was a very interesting lecture about dynamics. Until then, I never understood the power of the words such as dynamics, vibration and resonance. Of course, the example provided food for my thoughts to study more about how a bridge could fail due to lesser dynamic load compared to a heavier static load.

For those of you who are curious, the bridge was England’s Broughton Suspension Bridge that failed in 1831 due to the soldiers marching in step. The marching steps of the soldiers resonated with the natural frequency of the bridge. This caused the bridge to break apart and threw dozens of men into the water. Due to this catastrophic effect, the British Army issued orders that soldiers while crossing a suspension bridge must ‘break step’ and not march in unison.

Such failure has given rise to more emphasis on analyzing the structure (mechanical or civil) for dynamic loads if it undergoes any sort of vibrations. Traditionally test equipment have been used to experimentally monitor vibrations in new designs; this is costly however. We apply finite element analysis (FEA) to solve such problems. FEA solvers have evolved and today’s solvers are powerful not only in statics but in dynamics too.

Demystifying Modal Analysis

Modal Analysis: Getting Down to the Basics

In any dynamic/vibration analysis, the first step is to identify the dynamic characteristics of the structure. This is done through a simple analysis called Modal Analysis. Results from a Modal Analysis give us an insight of how the structure would respond to vibration/dynamic load by identifying the natural frequencies and mode shapes of the structure.

Modal Analysis is based on the reduced form of dynamic equation.

Demystifying Modal Analysis

As there is no external force acting and neglected damping, the equation is modified to:

Demystifying Modal Analysis

I have skipped the derivation part of natural frequency as it is easily available in textbooks. Natural frequency is substituted back into the equation to find out the respective mode shapes. These natural frequencies are the eigen values whereas the respective mode shapes are its eigen vectors. Natural frequencies & mode shapes in combination are called as modes.

Eigen vectors represent only the shape of deformation, but not the absolute value. That’s the reason it is called as mode shape. It is the shape the structure takes while oscillating at a respective frequency. Important point to remember is the structure has multiple modes and each mode  has a specific mode shape. If any load is applied with same frequency as natural frequency in the same direction as mode shape, then there will be increase in magnitude of oscillation. With no further damping, the scenario will lead to a failure due to a phenomena called resonance. To avoid this phenomena in dynamics, calculating the modes carries great importance.

Frequently-Asked Questions

Having said that, questions will certainly arise. In my opinion, these are the most commonly asked questions in support calls by customers using ANSYS.

  • Why do frequencies from simulation don’t match the test results?
  • Why are deformation and stresses in modal analysis very high?

From equation (3), it is clear that natural frequency of structure depends on its stiffness and mass. In order to accurately capture frequencies in FEA, the following points are important for you:

  • You need to capture mass of the structure and connecting/ignored members accurately.
  • Your mesh can be coarse, but enough refinement so that you can accurately capture the stiffness of the structure. If you are interested in the local modes in slender members, then you’ll need to perform local mesh refinement.
  • You need to define appropriate boundary conditions in forced modal analysis in order to capture realistic frequencies.
  • You need to accurately model the contact between different bodies in an assembly since they affect the stiffness of the structure drastically.

For the second question, a lot of confusion exists when the modes extracted in modal analysis show deformation magnitude. In Equation (2), you will see that no external load is applied on structure. This will make you wonder where these values come from? Let’s have a look with an example of simple cantilever beam.

Demystifying Modal Analysis
Fig. 1 – Mode shape & stress shape of Cantilever Beam

Fig. 1 shows its extracted mode shape 1 & stress shape 1 from modal analysis. I observe deformation to be as high as 253 mm and stress as 4,914 MPa which is far greater than the ultimate strength of Steel i.e. 500 MPa. You may wonder, why did we get these high values?

This happens because the FEA solver returns the mode shape (not the deformation magnitudes) as output. By this, I mean that magnitude of the mode shape is arbitrary (as seen in Fig. 1). The high value is because of a scale factor that’s chosen for mathematical reasons and does not represent anything real for the model. However this value helps us in relative measurement. Let’s take the example of the first mode. Maximum deformation occurs at the free end compared to any other location. This changes with the change in mode.

Since we have deformation, you can compute corresponding stresses and strains. Once again, these are relative values. If you ask the FEA solver for stresses & strains, it will use the same scaled deformation magnitudes and calculates stresses & strains. They are referred to as stress shape & strain shape (not to be confused with stress state or strain state) because no loads are applied. The magnitude of stresses and strains are useless but their distributions are useful to find hot-spots in the respective modes.

Conclusion

Modal analysis offer much more than just the frequencies and mode shapes. This analysis is primarily the stepping stone for linear dynamics studies to calculate the actual deformation due to different kinds of dynamics loads. Modal analysis has many secondary applications which I will discuss in my next blog.

 

Share this on:

Modeling Thermostats in ANSYS Workbench

This article talks about modeling thermostats in ANSYS Workbench using the COMBIN37 element that is quicker, sophisticated and automated. While working on a customer project, I struggled with the conventional approach in ANSYS 17.2. It was frustrating, and so I decided to look up for possible alternatives in the ANSYS Customer Portal. I found a solution using COMBIN37, however it only featured the option for switching OFF the heat source. Next I stumbled upon PADT’s ACT Extension, but again it didn’t seem to be useful for the problem described in this article. Thanks to the able support from ANSYS staff, I was able to obtain this solution.

Thermostats are used in many applications. For analyst, therefore, it is essential to understand the type and functionality of the component that she wants to replicate in the simulation environment. Before anything else, let me brief you about thermostats.

What is a thermostat?

Wikipedia defines thermostat as:

A component which senses the temperature of a system so that the system’s temperature is maintained near a desired set point.

Thermostats are widely used in varied industrial applications, however they are primarily used in heating and cooling systems. Air-conditioners, refrigerators, automotive coolant control, electric iron box, actuators, control valves are a few of the many applications. Thermostats are also used in many manufacturing processes to maintain the desired temperature limits.

Let us model it in ANSYS Workbench, shall we?

Problem definition

For this article, I selected a small block that is initially at room temperature. Also, this block is heated up by a source on the bottom face (possibly, a heater) that inputs 2W for 10 minutes. The objective is to maintain the temperature at a certain point (indicated with a red label; hereinafter referred to as sensor) on the body within 170-175°C.

Modeling Thermostats: Block geometry with a label for sensor
Block geometry chosen for this article
Possible solutions

In a simulation environment, there are two ways to model a thermostat.

Many beginners will adopt a conventional approach. In this approach, heat is fed to the surface until the temperature rise at the sensor reaches 175°C. However, the analyst doesn’t immediately recognize the time when the sensor attains 175°C. So she:

  • lets the computation run until the desired temperature is obtained
  • records the time at which this temperature is attained
  • divides the time dependent loads into number of load steps to control the switch ON/OFF
  • finally runs the analysis again until the temperature at sensor falls down to 170°C

This cycle continues for the entire analysis duration, however this example runs only for 10 minutes. For real problems, it is tedious and time-consuming to simulate the thermostat functionality to regulate the temperature with this procedure.

Modeling Thermostats with COMBIN37

Another solution for modeling thermostats is much quicker, more sophisticated and an automated technique. In order to make it work, it requires to build a connection between the sensor node and the heat source face to regulate the temperature. We introduce a temperature regulator that is modeled with element COMBIN37 in ANSYS.

Modeling Thermostats: Illustration depicting two humans aiming to plug one cable into another.

I’ll help you understand COMBIN37 – it is a unidirectional control element that has a capability to turn OFF and ON during the analysis. COMBIN37 has one set of active nodes (I,J) and one set of control nodes (K,L –optional nodes). Each node has one degree of freedom (DOF) that is valid for structural (translations/rotations/pressure) and thermal analysis (temperature). COMBIN37 has many other applications; you can find detailed information in the ANSYS Help manual.

Unfortunately, there is no direct way to model this in ANSYS Workbench. Of course, this entails a set of APDL scripts to run in the background. Simply put, 2 nodes, I & J of COMBIN37, are connected to the heat source face and sensor node respectively. Three arguments are defined using scripts – the first two define the range of temperatures on the sensor node and the third defines the heat flow from the source to be operated within the range defined – that indirectly defines the ON/OFF behavior.

Modeling Thermostats: Image showing the thermostat model used for this example. Heat source to switch off when temperate is 175 degree Centigrade and switch on when it drops to 170 degrees.
Operating range of the thermostat
Create COMBIN37 using commands

Let me now take you to the step-by-step procedure describing the commands used to model a thermostat in ANSYS Workbench.

Step 1

Since we’re creating an element that is possible only in pre-processor, you must enter the pre-processor.

/prep7                                                  ! Enter the Pre-processor

Step 2

Define the arguments for the magnitudes of temperature range within which the heat source should supply the heat. This definition of arguments will allow you to parametrize the temperature peaks as well. Parametrization allows for various studies such as sensitivity analysis, optimization or robustness evaluation in ANSYS Workbench. This can become important in coupled physics problems.

on_val=arg1                                       ! Set on_val to arg1

off_val=arg2                                      ! Set off_val to arg2

Step 3

Select the sensor node and obtain its node ID

cmsel,s,sensor                                    ! Select the named selection consisting sensor                                                                       node or vertex

sensor_node=ndnext(0)                 ! Get the node id

nsel,all                                                  ! Select back all nodes in the data base

Step 4

Create material ID for COMBIN37 element, define the type of DOF, ON/OFF behavior and input the third argument for heat flow with appropriate key options and real constants.

*GET,max_et,etyp,0,num,max        ! Get highest type attribute in the model.                                                                                  We increment this to ensure we are using                                                                                new, unique id for COMBIN37

et,max_et+1,37,,,8,,1                            ! Control element using temperature DOF and                                                                         set the thermostat behavior

r,max_et+1,,,,on_val,off_val,arg3  ! Set the on/off set points and the heat flow                                                                              rate

ex,1000,1                                                 ! Dummy material number/material id

Step 5

Create the nodes I & J for COMBIN37, and then create an element COMBIN37 by assigning these nodes to it.

*GET,max_nd,NODE,0,num,maxd         ! Get highest node id in the model

n,max_nd+1                                                   ! Create I node of the COMBIN37 – location                                                                               doesn’t matter

n,max_nd+2                                                   ! Create J node of the COMBIN37

type,max_et+1                                               ! Set the type to a next higher unique id

real,max_et+1                                               ! Set the real to a next higher unique id

mat,1000                                                        ! Set the material attribute pointer

e,max_nd+1,max_nd+2,sensor_node  ! Create COMBIN37 element

Step 6

Select the nodes of the heat source and couple these nodes to the node “I” of the COMBIN37. This coupling will copy all the information on the node “I” to the nodes on the heat source.

cmsel,s,Heater_Strip                               ! Select the named selection containing the                                                                              heat source face

nsel,a,,,max_nd+1                                    ! Also select the node I of the COMBIN37

cp,1,temp,all                                               ! Couple the temp DOF of the node I of the                                                                                COMBIN37 to the heater strip (source face)

nsel,all                                                         ! Select all nodes in the model

Step 7

Exit the pre-processor and enter into the solution

fini                                                                ! Finish out of pre processor

/solu                                                             ! Enter into the solution

Inputs

Once I input the temperature limits, heat flow and other applicable boundary conditions, I solve the model and check the results if the desired output is obtained.

Modeling Thermostats: Screenshot showing the result of the APDL commands that appear as Input Arguments for temperature limits and heat sourcei input in ANSYS Workbench
APDL commands appear as Input Arguments in ANSYS Workbench
Results Verification – Part I

From the results, temperature extracted for the sensor node must look as shown in the below figure. One can observe that the temperature on this sensor node rises initially to 175.01°C and then gradually falls down to 170°C. As soon as the temperature falls below 170°C, the heat supply will be automatically switched ON and ultimately this leads to the temperature rise on the sensor node. This cycle continues until the final time step of the analysis.

Modeling Thermostats: Image showing geometry of the problem along with the history of temperature over a period of 10 minutes.
Temperature vs. Time plot

In order to check if the applied heat flow is considered exactly as per the desired definition, use the User Defined Result and set the Material IDs as 1000 (refer the command script in Step 4) & use the Expression as SMISC2 (second sequence of element summable miscellaneous data – look into ANSYS Help for more info).

See the below figure for the definition and output of this user defined result. Remember! This result is possible to see only from the ANSYS Release 18.0, however it is available as a beta feature.

Modeling Thermostats: Screenshot of ANSYS Workbench showing the way to select material id and result expression as SMISC2.
Obtaining User Defined Result in ANSYS Workbench
Modeling Thermostats: Plot showing the history of heat flow input over time
Plot of Heat Flow Input vs. Time
Results Verification – Part II

To verify whether the heat input and the desired output are obtained based on the input definition, I plotted a chart with the temperature result on sensor and user defined result (heat input). In the below figure, the purple colored line indicates the heat flow while the green colored line indicates temperature rise on sensor. You will see that Heat Flow is OFF when peak (175°C-region between red arrows) is reached and ON as soon as it reaches 170°C (region between green arrows) and is constantly supplying 2W of heat. Hence the thermostat works!

Note: Y axis in the below figure is of normalized magnitude. This doesn’t imply the heat input as 1W.

Modeling Thermostats: Plot overlaying history of temperature and heat flow inputs over time. Using illustrations, the portion where heat flow is switched off and on is also shown.
Temperature & Heat Flow Inputs vs. Time
Benefits

This article aims to target analysts with beginner experience with modeling thermostats. As such, without using COMBIN37 element, analysts can tend to struggle for the end result for weeks because they have to manually regulate the temperature by re-running the analysis.

Using the COMBIN37 element, the solution is much quicker, more sophisticated and automated. For this study, it took hardly two hours of run time with the automated technique while the manual approach took roughly about 60 hoursThe extent of time savings is enormous using this element in ANSYS.

Suggestions

In summary, I suggest that you define the number of sub steps in a manner that the minimum time step is sufficiently low. As per this, when the time step is significantly high, the temperature increase or decrease will be of significantly larger magnitudes. As a result, temperature will keep falling out of the range.

Time step also becomes important in order to accurately capture temperature values especially when the range is quite small. Such instances are common in many industrial applications. For example, for the problem above described, the range is 170-175°C, i.e. difference of 5 degrees of centigrade. Accordingly I chose a suitable time step for this example.

Screenshot showing settings for analysis for modeling thermostats
Settings for Analysis

Before solving the problem, ensure that you have entered the inputs for the argument values in the Solver Unit System irrespective of the current/working unit system.

Share this on: