We are also excited to roll out this feature since it creates a second application to our learning simulations. That is enabling visualization and to some extent processing and interpretation of datasets. This gives the metaKinetic platform a multi-purpose functionality and adds an accessible, slick, and user-friendly tool in the technical team’s toolbox!

This feature will help users gain:

• A more customized learning experience using simulations
• A tool that takes your data from visualization to interpretation
• Effective communication across team members with different backgrounds

All that to say, we are taking interactive learning experience even further with our latest feature.

Taking interactive learning experience even further

The metaKinetic learning and development platform provides superior active learning experience for users. With more than 65 simulation-based courses spanning across multiple disciplines from Geophysics, Geomechanics, Petrophysics, Rock Engineering, to Reservoir Engineering, metaKinetic L&D is the largest collection of multi-disciplinary scientific applications designed for learning.

Want to learn more about the metaKinetic L&D platform and the data import feature? Contact us!

The post All Treats, No Tricks – New Feature Release: Data Import 🎉 appeared first on Meta | Innovative AI Analytics and Training Software.

SCORM, is an acronym for “Sharable Content Object Reference Model” and is an information standard for E-Learning that allows training content to become “portable” so it could be delivered and measured by different LMS platforms. SCORM compliant content can be created one time and used in many different learning management systems and situations without modification.

“This will allow for organizations with Learning Management Systems in place to incorporate metaKinetic’s simulation-based courses in a plug-and-play fashion. The organizational learning and development managers and training officers will enjoy the easy import of content in addition of consolidating their tracking and reporting in their current LMS platform.” says the Chief Technology Officer, Brandon Reid.

Committed to Strengthening Offering to Our Customers

The metaKinetic learning and development platform provides superior active learning experience for users. With more than 65 simulation-based courses spanning across multiple disciplines from Geophysics, Geomechanics, Petrophysics, Rock Engineering, to Reservoir Engineering, metaKinetic L&D is the largest collection of multi-disciplinary scientific applications designed for learning.

“The SCORM compliant metaKinetic’s simulations will allow us to service the larger organizations and enterprises that have been using certain learning management systems for a long time with a seamless content transfer. Meanwhile, small and midsize organizations with no current LMS can enjoy the metaKinetic L&D platform as their go-to learning management system. This compliance guarantees and strengthens our offering to our customers regardless of their organization’s size and current learning and development setup.” says the VP of Sales, Jonathan Charles.

Want to learn more about the metaKinetic-L&D platform? Contact us!

The post metaKinetic Simulations Are Now SCORM Compliant! 🎉 appeared first on Meta | Innovative AI Analytics and Training Software.

Rock Mass Classification

Rock engineering design initially requires a rock mass classification of the domain. As cited in Hoek (2007) rock mass classification schemes have been developed for over 100 years since Ritter (1879) attempted to formalize an empirical approach to tunnel design, in particular for determining support requirements.

An example of a rock mass classification system that is commonly used by rock engineering practitioners is Rock Mass Rating (RMR). Bieniawski introduced the RMR in 1973. Since its introduction, there have been multiple revisions to the relative weighting of its input parameters. The most commonly used RMR is RMR₇₆ and RMR₈₉.

RMR simulation shown below allows learners to adjust the five rock mass parameters of intact strength, rock quality designation (RQD), joint spacing, joint condition, and water content to predict both RMR solutions.

This simulation provides a RMR value based on bin solutions. That is the typical data collection method when in the field. The simulation allows for the learner to easily see and identify the differences between the two RMR systems and more importantly visualize the role of each parameter. 

Tunnel Failure Mechanism

A common mistake that inexperience rock engineering practitioners make is only using the value of the rock mass classification system to select a tunnel support system. Each parameter used to weaken the rock mass can have a diving factor on the potential failure mechanism. Tunnel failure simulation shown below allows professionals to quickly visualize the effects of changing rock mass classification and intact conditions on the failure criteria and expected tunnel failure mechanism. This solution is a function of stress, intact rock properties, and rock mass classification parameters.

This simulation has been effective to illustrate to learners that the same rock mass classification value can lead to different tunnel failure mechanisms that require different support systems and sequencing. Here the expected failure conditions are estimated based on the work of Marinos (2013); for all failure conditions expect spalling and rock bursting. Rock bursting and spalling are based on the work of Kaiser et al. (2000).

Rock Mass Parameterization

Once the failure mechanism and rock mass classification are understood, learners can develop rock mass parameters to be used in numerical or analytical analysis. These parameters can be used to estimate joint properties or the rock mass properties. For most shallow hard rock mining applications (<1000 m), a support design can be governed by kinematic analysis (i.e. joint properties and orientation). However, for deeper and/or weak rock mining/tunneling applications the assessment of the potential convergence on the support system is required. In order to estimate the convergence, rock mass parameters are required.

Figure 3 illustrates a simulation for rock mass parameterization. The rock mass classification is based on the intact Hoek-Brown input parameters and the Geological Strength Index (GSI). The simulation outputs the rock mass parameters for Hoek-Brown and Mohr-Coulomb constitutive models.

Rock mass parameterization simulation guides learners through each parameter and their influence on the constitutive models. The most important lesson that is illustrated within the simulation is that the rock mass classification value (i.e., GSI) can be adjusted so that the value is the same, but parameters used to calculate the value change. When the GSI value is constant, there is no change to the failure criteria curves. When learners understand this concept, they understand the tunnel failure mechanism more thoroughly, reinforcing the previous simulation learning outcomes.

Ground Reaction Curve

To capture the potential convergence of the tunnel, a ground reaction curve (GRC) is required to be developed. A GRC simulation, as shown below, captures the effects of intact rock parameters, rock classification, and in-situ stress conditions.

The GRC relates internal pressure to displacement (convergence) of the tunnel walls and is one of the three components of the convergence-confinement method (CCM). The GRC only considers the internal pressure caused by the yielding rock mass, and does not consider loads caused by rock wedges. Kinematic analysis would need to be considered and compared at this stage to see which is the driving factor.

Support Response Curve

The second component of the CCM, is the support response curve (SRC).  The SRC relates deformation (confinement) of the support pressure on the convergence of the tunnel. As shown below this simulation allows for learners to explore how each of the different supports used in typical tunnel application influence the SRC. The ductility of the bolts compared to the rigidity and brittleness of the shotcrete liners is easily illustrated.

Longitudinal Displacement Profile

The third component of the CCM, is the longitudinal displacement profile (LDP). The LDP relates tunnel wall displacement to the position of the tunnel face. Two LDPs are presented within the simulation shown below . The first being Vlachopoulos and Diederichs (2009), which is most commonly used in industry due to its simplicity. The second being Oke et al. (2018) which took the analysis of Vlachopoulos and Diederichs (2009) and refined the mesh and the boundary conditions to create a more precise solution of the amount of displacement that occurs at the tunnel face.

Convergence-Confinement Method

With a supported LDP, it is possible to combine the GRC and the SRC to complete the convergence-confinement method through an iterative process. Combining the three components allows for the identification of the analytical capacity of the support and visualization of the pre-convergence required before support installation as shown below. This pre-convergence is very important to understand when 2D numerical simulations are being conducted. 2D analysis requires pre-convergence to get a capture a realistic support response. Based on the support response, rock engineers are able to assess the support efficiency.

Path to Successful Rock Engineering Training

Studies show leading learners through a query-based workflow increases the overall confidence on understanding ground response and support design methodology. For more related simulations visit Rock Engineering Gallery.

Rock engineering simulations can provide professionals with incremental, query-based, and guided learning path that takes them from data collection to tunnel support design. This prevents, the rock engineering falling within the classic error of not understanding all of the steps within the design process and mitigating the risk of inputting the wrong data (e.g. creating “garbage in garbage out” situations) or conducting a not applicable analysis. These simulations increase the confidence and understanding of the learner and resulting in more effective support designs.

Dr. Jeffrey Oke, Industry Advisor

Multiple case studies shows that leaners in industrial or academic setting find simulation-based training extremely effective in learning and retaining technical concepts. Want access to these simulations and more on the metaKinetic platform? Contact us!

The post A Hands-on Learning Path for Rock Engineers appeared first on Meta | Innovative AI Analytics and Training Software.

The metaKinetic platform offers a hands-on approach to learning technical geoscience and subsurface engineering concepts through simulation-based courses. The platform equips its users with real life experience with over 60 simulations.

Included in the launch of metaKinetic 2.0 are many exciting new additions including:

60+ Simulations Across 6 Sub-Disciplines

With a large variety of interactive simulations to choose from, the metaKinetic platform is sure to offer the most used topics in Geosciences and Engineering for both corporate and academic settings. Meta is proud to offer simulations in the following sub-disciplines referred to as galleries: 

• Active Seismic
• Passive Seismic
• Geomechanics
• Petrophysics
• Reservoir Engineering
• Rock Engineering

Each gallery offers ever-green content and each course features a complete tutorial, technical glossary, references, and assessments; all in one place to transform the way learning is done.

Some of the most recent additions to the platform include the “Phase Envelope” simulation which allows users to define different phases of chemical mixtures and calculate its corresponding phase envelope as well as the “Well Performance Modeling” simulation that enables users to calculate IPR and VLP relationships to determine an estimate of the well deliverability.

More of the most popular simulations on the platform can be found here.

Feedback Feature

The new Feedback Feature in metaKinetic 2.0 allows for users to be able to suggest new simulation-based courses they would like to see added to the platform as well as sharing any feedback directly with the product development team. For a more in-depth look into this new feature, check out the Feedback Feature explainer video here.

Corporate Admins/Educators Dashboard to Track Users Data

metaKinetic 2.0 includes the first time release of a new “Admin Dashboard”. With the addition of this dashboard getting insights on the user’s learning process has never been easier. In other words, corporate administrators, training officers, and educators, easily get a better feel of how training impacts and relates to learners’ performance and where they need more help.

Improved Features

The existing features on the metaKinetic platform have also been improved in the metaKinetic 2.0 release. These features include:

• Profile: Add your personal information to your account and track your progress.
• Guide-Thru: Get a step-by-step guide of the simulation you’re working on to ensure you’re learning the most information possible.
• Full Screen: Work on simulations in full screen for joint learning or for complete focus of your work.
• Search: Quickly find simulations you’re looking for to fast track getting done what’s most important to you.
• Peer-Benchmark: See where you stand next to your peers or classmates.

To learn more about these features, click here. 

“metaKinetic gives the opportunity to take charge of your own learning and make learning an active process.”

– Alexander Braun, Professor @ Queen’s University

About Meta Innovation Technologies

Meta is the leading tech company in the Energy and Mining sectors. metaKinetic Learning and Development (L&D) platform is the first digital platform of its kind built for learning Geosciences and Engineering to power technical managers and their teams as well as educators with intuitive learning, quick takeaways, hands-on practice, and skills uplift.

Contact us!

The post metaKinetic 2.0: Meta’s Most Innovative Simulation-Based Training Platform Yet appeared first on Meta | Innovative AI Analytics and Training Software.

DCA is an empirical graphical technique which is used to forecast oil and gas production and estimate the remaining reserve. The goal of DCA is to extract useful information for asset management purposes from production rate measurements.

Empirical DCA is one of the most popular methods to forecast production and estimate EUR from previous production. While Equations derived by Arps have been the backbone of DCA for several years, in recent years new DCA methods were proposed by others to predict rate with higher certainty especially in unconventional reservoirs.  

Hamza Ali, Reservoir Engineer

Popular Decline Curve Models

1. Arps

Arps’ DCA is an empirical method based on the plot of flow rate versus time to the abandonment time. There are three types of production rate decline characterized based on the way in which rate declines with time: hyperbolic, harmonic, and exponential. 

where q_i is the maximum production rate (usually is equal to instantaneous rate at time 0), q is the instantaneous producing rate at time t, D_i is the initial decline rate at time 0, and b is the power exponent which controls how the change of the decline rate with time. The value of b is 1 and 0 for harmonic and exponential declines, respectively. b changes in the range of 0 to 1 for hyperbolic decline. For unconventional resources sometimes b values greater than 1 is used for analyzing production data.

2. PLE

Power Law Exponential was developed by Ilk et al. in 2008 to forecast production in shale reservoirs.   

where D_∞ is decline rate at large times, D₁ is initial decline rate, and n is hyperbolic exponent (0 <= n <= 0.5).  

3. SEPD

SEPD was proposed by Valko in 2008 and is very similar to PLE. The only difference between SEPD and PLE is in the late time of production because for SEPD method the D_∞ is equal to zero.  

4. Duong

Another method to forecast production in shale reservoirs was proposed by Duong in 2011. 

where a and m are the slope and intercept of the rate over cumulative production plotted versus time, respectively. 

5. LGM

Logistic Growth Model (Clark et al. 2011) is based on the concept that decline rate grows only to a certain size in time. In DCA analysis the maximum recoverable reserve is EUR. The sum of the remaining reserve and cumulative production for a fixed economic rate limit must not exceed EUR. The maximum growth size (in this case EUR) is called carrying capacity, K. 

where K is carrying capacity, n is a parameter than controls the steepness of the decline curve, and a is the decline exponent.

Above is a DCA simulation hosted on the metaKinetic platform. Using this application you can determine the cumulative production, remaining reserve, and estimated ultimate recovery (EUR) from graphical decline curve analysis using all models named above in different phases such as gas, oil, water.

Want access to this simulation and more on the metaKinetic platform? Contact us!

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