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.

Applications of Hydraulic Fracturing

The most common application of hydraulic fracturing is to stimulate the reservoirs with low permeability to increase production but hydraulic fractures are also used for other purposes such as increasing injectivity for water flooding, waste disposal, in-situ thermal operations and also mitigating the formation damage caused by drilling or cementing.

While using hydraulic fracturing for enhancing hydrocarbon recovery was started several decades ago, its recent application for production from low permeability rocks through multi-stage fracking has made it the center of attention. Multi-stage fracking consists of creation of several hydraulic fractures along the horizontal section of a well.

Geometry Modeling of Hydraulic Fractures

The purpose of fracture geometry modeling is finding the fracture geometry which the injected fracturing fluid occupies while accounting for the share of the leakoff fluid lost to the rock. Actual fracture geometries can be overly complex, but in their simplest form, fractures are identified as planar volumes in specific orientations. The geometries of these simple fractures are usually identified by their shape, height, length, and width.  

“There are different tools for frack geometry modeling that range from simpler analytical models that assume simple geometries for a fracture to semi-analytical and numerical models that consider more complexities in the geometry of the fracture. The two models provided in this simulation, KGD and PKN, are popular analytical models that use plane-strain elastic solutions to find fracture geometry.”

Mehrdad Soltanzadeh, Industry Advisor

KGD model

This model is based on Kristianovich-Geertsma-de Klerk work who developed this analytical solution for the geometry of a hydraulic fracture. This plane-strain solution assumes a constant height for the fracture throughout its entire length. The thickness of fracture remains constant in each vertical section but it varies along the fracture. This geometry is considered more representative for the fractures that their heights are considerably more than their lengths. 

PKN model

This model is based on Perkins-Kern-Nordgren work who developed this analytical solution for the geometry of a hydraulic fractures. This plane-strain solution calculates a varying height for the fracture throughout its length. The thickness of the fracture varies both in the vertical section and along the fracture length. This geometry is considered more valid if the length of the fracture is considerably more than its height.

Below  is the “Hydraulic Fracture Geometry Modeling” simulation hosted on the metaKinetic platform. Using this application you will be able to construct analytical models for fracture geometry using different methods with Newtonian and non-Newtonian fluids.

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

The post What Is Hydraulic Fracturing and Why It’s Important to model them? appeared first on Meta | Innovative AI Analytics and Training Software.

Let’s start with some fundamentals. Hydraulic fracturing is accomplished through initiating a weakness point (or several) around a well. One approach commonly used is through the use of a perforation gun, and then pumping high pressure fluids and particulates (proppant) though the well at these entry points (commonly referred to as perfs or perf zones) extend into hydraulic fractures away from the treatment well. These hydraulic fractures will interact with the pre-existing network or fractures in the medium, and will generate a slip on these fractures. The zone of “influence” of the stimulation can be potentially outlined based on the observed fracturing that occurs under dynamic stress conditions.

Stimulated Reservoir Volume (SRV)

If the SRV can be estimated accurately from microseismicity, then the operators can predict how much hydrocarbon they may produce with time and gain accurate estimates of Net Present Value (NPV) after hydraulic fracturing. Other information form other data streams are integrated with the estimated SRV at this step. Beyond the economics of the production size, understanding how far away from the well primary production may be expected, and along what azimuth, informs an operator on how to drill their child wells to complete their asset.

The Holy Grail

A production engineer will usually assume that primary production (that is, the early production that comes through the well with the highest rate) is dominated by the cracks that have received proppant, so called “propped volume.” The “holy grail” of microseismic monitoring of hydraulic fractures is therefore to determine which events represent fractures that will receive proppant from the other cloud of events. While mapping proppant distribution is a lofty aspiration, a useful lower-hanging fruit is to determine events that are showing more connectivity in close proximity to the treatment well, which are more likely fluid influenced fractures versus other that are stress induced. By focusing on these fluid-connected areas, plausible volumes of production can be obtained, resulting in better estimates for SRV.

Above is the “Completion Evaluator” simulation hosted on the metaKinetic platform. Using this application you can explore activated fractures identified through microseismic distribution, and examine inferred modes of failure and dynamics of stress/strain field, and witness their relationship to treatment parameters such as pressure, slurry rate, and proppant concentration.

Beyond Accurate SRV Estimates

Fracturing and stress behavior are paramount to understanding the effectiveness of stimulation programs. As differences in injection rates, pressure, and fluid and proppant types occur during a stimulation, the rock fracturing response is directly related to the dynamic stress conditions that make it ideal for different fracture sets to be activated. Tracking fracture orientations, failure types, and localized stress orientations provides the impetus for defining the roles of different injection parameters. 

The microseismic response of different treatments may be used to understand how to effectively stimulate a reservoir, and adjust the stimulation parameters based on the reservoir response. Further to the optimization of stimulation parameters, optimally all the fluid and proppant will be contained within “zone”, the targeted hydrocarbon-bearing lithological formation. Sparse events out of zone are often not indicative of fractures that are connected to producing region. 

Microseismicity, if well located and characterized, can outline the effectively stimulated regions, and can therefore be used to adjust the stimulations to avoid out-of-zone growth. The discrimination of regions that have not been effectively stimulated within zones of stimulation – the so-called “bypass” zones – can be identified as areas of relative quiescence. Once identified, these regions can be targeted with further development of the asset.  

Ted Urbancic, Scientific Advisor

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

The post Stimulated Reservoir Volume Explained appeared first on Meta | Innovative AI Analytics and Training Software.

Classification of the AVO response enables geoscientists to arrive at better interpretation of reservoir rock properties.

The first applications of AVO methodologies were in the context of gas sands. Rutherford and Williams (1989) systematized gas sand behavior into three classes based on their amplitudes and gradients. It was further elaborated by Castagna and colleagues, almost a decade later, who added a fourth class. In this blog post we provide a concise description of different AVO classes.

The Different AVO Classes

Class 1

A Class 1 anomaly is due to low impedance shales over high impedance gas sands that result in positive amplitudes at low angles that decrease amplitude with increasing angle. They may change polarity at far enough angles. This is more common with onshore, hard rock, mature sand with moderate to high compaction. In terms of the Shuey terminology this is a positive intercept and a relatively large, negative gradient.

Class 2

A Class 2 anomaly from a near-zero impedance contrast. That is, the overlying shale has similar properties to the underlying gas sand that is moderately compacted and consolidated. The AVO response is a small positive or negative amplitudes which likely falls to negative values – that is, a polarity change at some higher incidence angle. The AVO attributes low positive or negative intercepts and negative gradients generally smaller than Class 1.

Class 3

A Class 3 anomaly results from higher impedance shales over low impedance uncompacted and unconsolidated gas sands causing large negative amplitudes which increase in magnitude as the angle of incidence increases. On a Shuey attribute plot, this is seen as a relatively high negative intercept and a negative gradient smaller than Class 2. However, since the amplitude magnitude increases with offset, stacked amplitudes can be very large and can result in the ‘bright spots’ of earlier AVO interpretation.

Class 4

A Class 4 anomaly is a special case of Class 3 as it involves low impedance, uncompacted and unconsolidated gas sands overlain by high impedance cap rocks like hard, silaceous or calcareous shales, siltstones or tightly cemented sands or carbonates. Here, negative reflection coefficients decrease their magnitude with angle giving Shuey attributes of a negative intercept and a small positive gradient. The Class 4 is the only anomaly with a positive gradient.

While this classification can be useful, it is a very limited way to interpret AVO results since it is quite specific to only gas sands and is not general at all. But it is a very well-known categorization and important from at least an historical perspective. In a general context, most AVO behavior falls into ‘Class 1’ and ‘Class 2’ but there is no implication that such behavior is distinctly indicative of a gas sand. 

Caveat Emptor! Interpretations need to be made in proper context. If there is a geologic context for suspecting gas sand reservoirs, then this framework might be useful.

Michael Burianyk, Scientific Advsior

Above is an AVO Classes simulation hosted on the metaKinetic platform. Using this application you will become acquainted with the concept of AVO classes for gas sands. It categorizes the changes in the Shuey attributes as the gas sands soften in relation to their overlying cap rocks based on velocity input.

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

The post AVO Classification appeared first on Meta | Innovative AI Analytics and Training Software.

Welcome to The Next Normal!

Under the new economic climate, effective technical communication among the staff are far more important than ever to ensure agility, efficiency, and higher team performance. The effective technical communication often comes with multi-disciplinary knowledge and skillsets in addition to visual tools that can aid explanations and encourage collaboration.

“metaKinetic platform, for learning & development (L&D), is purpose-build to deliver on expanding horizons of technical knowledge by interacting with concepts and data visually, regardless of technical background.

We are excited for adding another sub-discipline to our current offerings. The wait for those who wanted to get hands-on experience of Reservoir Engineering is over!”

Ellie Ardakani, CEO & Cofounder

metaKinetic-L&D platform currently provides 50+ simulations under topics such as Active Seismic, Geomechanics, Passive Seismic, Petrophysics, Rock Engineering, and now Reservoir Engineering.

Over the course of the last few weeks metaKinetic-L&D released the first four simulations on the hottest and most used concepts and workflows of Reservoir Engineering which are available to all current users including “Decline Curve Analysis”, “Gas Material Balance”, “PVT Analysis”, and “Gas Pseudo-pressure”.

Upcoming courses and simulations for August include “Relative Permeability”, “Darcy’s Law” and “Skin Factor”, and “Bottom Hole Pressure”. Contact us if you would like to access these simulations and more on the metaKinetic platform.

The post Launching Reservoir Engineering Gallery appeared first on Meta | Innovative AI Analytics and Training Software.

In petrophysical and geophysical applications, however, knowledge of how strain amplitude and stress conditions influence uniaxial loading stiffness as well as Poisson’s ratio is necessary.

Complexity of YM & PR determination

The data collected in the lab demonstrate how much complexity lies in the determination of the Young’s modulus (YM) and Poisson’s ratio (PR). This data can be used to decide what values should be kept as reference for a given material, before other effects such as complex states of stress can be quantified.

Below is a simulation hosted on the metaKinetic platform that represents such lab data and provides an interactive estimation of YM and PR.

Looking only at the major cycles, the first loading or ‘virgin loading’ is where the smaller Young’s modulus values are traditionally obtained. Upon subsequent unloading and reloading, the samples always exhibit a stiffer behavior up to the highest stress level that was encountered in the first loading. 

The low stiffness measured on this initial loading leg is usually related to either:

1. The closure of stress relief induced microcracks or other microstructural disturbances,
2. Strain hysteresis which causes loading-unloading loops that become more noticeable as strain amplitude increases. 

Hysteresis

Hysteresis causes both Young’s modulus and Poisson’s ratio to create ‘bow tie’ patterns as showed in the above simulation in yellow. Though this behavior can be satisfactorily modeled, it is hardly conducive to the definition of a single pair of YM and PR values for a given stress state.

The moduli obtained during the small unloading and reloading cycles shown in green appear to be independent on the loading cycle in which they were measured. Those moduli not only are independent of the loading cycle but also are the only ones that actually qualify as linear elastic moduli as they exhibit a reversible stress-strain behavior. Therefore, these small perturbation moduli are the ones that one should focus on in a first instance. They can constitute ‘seeds’ from which a more complex behavior (i.e. accounting for hysteresis) can be modeled if desired. 

Where is the difference between the static and dynamic moduli coming from?

This mostly is attributable to the difference between drained and undrained behaviors. In drained conditions, the deformation is slow enough to allow saturating fluids to move through the pore network without affecting the stiffness. In undrained conditions, the high strain rate associated with the propagating wave mostly precludes fluid motion which results in the fluid directly contributing to the overall stiffness by an amount commensurate with its bulk modulus scaled by the rock porosity. In the absence of a saturating fluid, the small perturbation static moduli can fall very close to the dynamic ones. Keep in mind that many other parameters can strongly influence these results such as fluid type, fluid saturation level, and material anisotropy.

Laurent Louis, Scientific Advisor

Contact us if you would like to access this simulation and more on the metaKinetic platform.

The post Elastic Properties in Lab Data 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!

The post Decline Curve Analysis appeared first on Meta | Innovative AI Analytics and Training Software.