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Probabilistic approach to benchmarking three numerical simulators Using the Egg Model

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Probabilistic approach to benchmarking three numerical simulators Using the Egg Model A large number of the decisions taki...
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Probabilistic approach to benchmarking three numerical simulators Using the Egg Model








Probabilistic approach to benchmarking three numerical simulators Using the Egg Model

A large number of the decisions taking by oil & gas companies rely on the evaluation of simulators to project resources and hence infrastructure, production, wells and operations. These simulators integrate rock physics and fluid dynamics to give a more realistic representation of the reservoir and its response during the field development. The inputs used for the simulators vary but mostly rely on data from geology, seismic as well as exploration and production in order for the models to be as accurate as possible.

Laboratory investigation of Surfactant and Polymer formulation design for Heavy Oil recovery in sand stone reservoirs

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Laboratory investigation of Surfactant and Polymer formulation design for Heavy Oil recovery in sand stone reservoirs Chemical flo...
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Laboratory investigation of Surfactant and Polymer formulation design for Heavy Oil recovery in sand stone reservoirs


Laboratory investigation of Surfactant and Polymer formulation design for Heavy Oil recovery in sand stone reservoirs


Chemical flooding is classed as an important technique of Enhanced Oil Recovery (EOR) which has been applied throughout the globe since the 1980s. The outcomes of such flooding have mainly been effective in both onshore and offshore locations with the latter being more challenging due to factors such as weather; challenges associated with logistics of chemicals and high salinity content in sea waters. Nonetheless, there has been significant incremental oil recovered in offshore location especially during the last couple of decades (Manrique 2007). The contributing factors behind the success rate of such flooding are mainly due to the combination of chemicals used. The surfactant and polymer used in chemical flooding would help in producing more oil which was previously out of reach through various mechanisms such as the lowering of Interfacial Tension (IFT) between the phases in the reservoir as well as improving the sweep efficiency. 

Vast amount of research has been done on chemical flooding, however, the effect of various combinations of chemicals used in Alkaline, Surfactant, Polymer (ASP) flooding on the mechanism behind changes in the capillary pressures have not been fully clarified (Shen, 2007).

Research carried out by Zhang et al. (2012) indicated that it was possible was to develop an ASP flood which could recover incremental oil at low surfactant concentrations. They performed a series of phase behavior tests, interfacial tension (IFT) measurements and core flood test in order to develop a solution for oil that had an API gravity of 15 and viscosity of 2,000 cp. The result indicated an incremental recovery of 6 % in comparison with previous water flooding (Zhang, 2012).

Furthermore, in recent research by Sheng, 2013, the need for development of ASP formulation with high temperature and high salinity limit has been highlighted. The advantage of ASP formulation over its individual process is the synergy between the polymer, surfactant and alkaline used. To have this synergy, the components of alkaline, surfactant and polymer must be in the same slug (Sheng, 2013). For example, in alkaline-polymer flooding, alkaline reaction with crude oil results in soap generation, wettability alteration, and emulsification; and polymer provides the required mobility control. Alkaline-polymer flooding can displace more residual oil than individual alkaline flooding or polymer flooding (Sheng, 2013).


Moreover, it was reported by Sheng, 2013 that when surfactant and polymer were injected in the same slug (SP flooding), their compatibility was an issue. In some cases, polymer was injected before surfactant as a sacrificial agent for adsorption or for conformance improvement. Sometimes polymer was injected behind surfactant to avoid chase water fingering in the surfactant slug. Even though polymer was not injected with surfactant in the same slug, they mixed at their interface because of dispersion and diffusion. Polymer may also mix with surfactant owing to the inaccessible pore volume phenomenon when it is injected behind surfactant. Sheng referred to these phenomena as surfactant-polymer interaction or incompatibility. Cases such as this are examples of the possible interaction that polymer and surfactant can have once injected into the reservoir. These interaction sometimes have negative or positive effect of the performance of the chemicals.

In other researches, the effectiveness of associative polymers in displacing of oil has been reported (Wever, 2011). The main attraction of associative polymers for EOR is their significant viscosity enhancement ability compared with conventional polymers and their potential salinity resistance in real oil field applications (Wever, 2011).

An example of an associative polymer is Phenyl-polyacrylamide (PPAM) which is a hydrophobic synthesized Polyacrylamide (PAM). Such synthesized polymers can be used as a more effective mobility control agent in future research. It is worth mentioning that PPAM is a non-commercial type of polymer which has been developed in laboratory at London South Bank University. The results of recent tests carried out at LSBU showed that PPAM had higher resistance to salinity conditions and also higher viscosity at the same concentration compared to PAM. 



Research Aims


The aim of this research is to study the synergy between the use of hydrophobic modified acrylamide and series of surfactants. This would be achieved through understanding the synergy between the candidate surfactants and polymers such as PPAM used in the core flooding. The subsequent findings around the synergetic effects will be used to design an effective chemical flooding more resistant to salinity and thermal conditions with the view of achieving higher efficiency in fluid displacement.

Research Objectives


·         Study in detail the chemical composition and structure of candidate polymer, surfactant and alkaline
·         Understand in details the synergy between the candidate polymer, surfactant and alkaline.
·         Develop an ASP formulation, with tolerance to high salinity and high temperature conditions, based on the understanding around the synergy of candidate polymer, surfactant and alkaline.

Research Methodology


Experimental work

It is of importance to embrace the candidate reservoir characteristics in order to design EOR fluid. One way of accomplishing this is through laboratory analysis where fluid-fluid and rock-fluid interactions are evaluated. Doing so, it is possible to introduce solutions tailored to each candidate reservoir.

Series of laboratory experiments are listed below:

·           Injectivity and sweep efficiency (core flooding)
·           Filterability test: To evaluate the presence of undissolved solids in the SP solution and to identify a possible plugging behaviour.
·           Viscosimtry test: Provides the viscosifying power of the polymer solution, allowing the calculation of polymer consumption (rheometer)
·           Adsorption: Evaluates the adsorption levels in core sample (UV-spectrophotometric.)
·           Resistance factor: Evaluates the possibility of reduction in permeability due to polymer injection, if any.
·           IFT measurement: Determine the optimum concentration of the SP solution (tensiometer).
·           Thermal Stability test: evaluate the performance of SP solution at various temperatures
Chemicals under evaluation

·         Primarily, the already available chemicals such as Phenyl-polyacrylamide (PPAM), Hydrolyzed Polyacrylamide (HPAM) and KYPAM will be used in experiments.
·         Surfactants such as Linear alcohol ethoxylates, Nonylphenol ethoxylates, petroleum sulfonates and synthetic alkyl sulfonates, Alkyl Ether Carboxylates

Simulation modelling

A probabilistic approach will be taken toward the findings of this project by means of embracing the uncertainties associated with subsurface and surface elements. Through Risk and uncertainty analysis it would be possible to reduce the error margins. This can be achieved by means of Experimental Design in order to take into consideration all possible development scenarios of a project.


References

§  Sheng. J, 2013. A Comprehensive Review of Alkaline-Surfactant-Polymer (ASP) Flooding. Paper SPE 165358, presented at the 2013 SPE Western Regional &AAPG Pacific Section Meeting, California, USA, 19-25 April 2013.
§  Manrique, E; Muci.V.E; Gurfinkel, M. “EOR Field Experiences in Carbonate Reservoirs in the United States”. SPE 1000063, paper presented at the 2006 SPE/DOE Symposium on Improved Oil Recovery held in Tulsa, Oklahoma,  22-26 April, 2006.
§  Shen, P., Wang, J., Yuan, S., Zhon, T., Jla, X., “Study of Enhanced-Oil-Recovery Mechanism of Alkali/Surfactant/Polymer Flooding in Porous Media from Experiments “.  SPE 126126 presented at the International Petroleum Technology Conference held in Dubai, 4-6 December 2007.
§  Wever, D., Broekhuis, F., Polymers for enhanced oil recovery: A paradigm for structure-property releationship in aqueous. Pogress in Polymer Science. 36,11,p.1588-1628, November 2011.
§  Zhang, J., Ravikiran, R., Freiberg, D., Thomas, C., 2012. ASP Formulation Design for Heavy Oil. Paper SPE 153570, presented at the Eighteenth SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, 14-18 April 2012.

Author.

Arash Farhadi.

Uncertainty evaluation applied to a stochastic history match study -White Paper-

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With current pressured market condition, understanding the uncertainties and risks associated  with a particular project is c...
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Uncertainty evaluation applied to a stochastic history match study -White Paper-




With current pressured market condition, understanding the uncertainties and risks associated  with a particular project is crucial when an investment decision have to be taken.

Uncertainty is a state of having limited knowledge of current conditions or future outcomes. It is a major component of risk, which involves the likelihood and scale of negative consequences, in order to minimize the risk that managers’ decisions will lead to undesired outcomes a well-supported decision frame have to be placed, using all possible resources and techniques including stochastic assessments base on simulation models.

This white paper describe a proved methodology used to evaluate and quantify subsurface uncertainty in a reservoir model used as basis for decision making.

http://www.primerareservoir.com/resources/Primera%20Reservoir%20White_Paper_UNISIM_UA_Stochastic_History_Match.pdf


Author:
Jesus Aponte
Reservoir Engineering Consultant at
Primera Reservoir LTD

Making Decision Under Uncertainty

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Effective management of uncertainties and life-cycle risks is critical to the success of oil and gas projects. The uncertainties and ri...
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Making Decision Under Uncertainty



Effective management of uncertainties and life-cycle risks is critical to the success of oil and gas projects. The uncertainties and risks are, in fact, the central challenge of the Exploration and Production business.

In May of 2014 Alejandro Primera was invited to give a talk about the relevance of capturing the uncertainties associated with reservoir characteristics. The work included Experimental Design (ED) and the modelling of reservoir uncertainties in a numerical simulation. The aim of the talk was to provide the audience with examples and the experience of making decisions under high subsurface and surface facility uncertainty and risk associated.


The topics discussed by Alejandro Primera emphasized on the correlation of the decision making process with the uncertainties present in the process. Because of the mix of subsurface, surface and economic uncertainties, the application of ED sampling to studies could be a cumbersome process, by which multiple models have to be coupled, so a strict process for the configuration of studies is far from been established in the industry, on top of the large number of parameters which need to be taken into consideration. Hence, Alejandro underlined that the complete evaluation of subsurface and surface uncertainty usually depends on geological, economic and technological uncertainties which can impact, in various degrees, the recovery process and the project viability. 

During the talk, ED examples were shown to the audience to envisage the level and range of uncertainties which can be incorporated into simulation modelling. In addition, the interaction between the different elements affecting critical decisions making in a field development scenario was highlighted. Furthermore, the differences between technical and organisational complications when choosing between different development options were also discussed.


More about ED

ED can play an essential role when it comes to development of ranking and uncertainty analysis studies in several industries. In a set of experiments, we intentionally change one or more input parameters in order to observe the effect the changes have on one or more responses or Key Performance Indicators (KPIs). It is important to plan and develop the studies, ensuring that the right type of data and sufficient sample size and power are available to answer the research questions of interest as clearly and efficiently as possible. There are several commercial and open source tools that could be used to select experiments sets and fit linear, quadratic or more complex models.













Figure 1 : ED sample workflow


In Oil and Gas, Numerical simulation of subsurface fluid flow coupled with surface infrastructure is a time consuming exercise. Computer experiments with quantitative factors require special types of ED, it is often possible to include many different levels of the factors. Also, the experimental region is often too large to assume that a linear or quadratic model adequately represents the phenomenon under investigation. Consequently, it is desirable to fill the experimental space with points as well as possible (space-filling designs) in such a way that each run provides additional information even if some factors turn out to be irrelevant.


Ranking Designs

In every project the range and type of input parameters will differ. However, what remains essential is the identification of most influential parameters.  It is important to analyse the effect of each parameters, whether positive or negative, and the magnitude of the effect on the KPI.

 Figure 2 : tornado plot indicating the significance of each input variable on the NPV at $100/bbl.


Proxys and Monte Carlo Simulation

One of the advantages of ED, is the possibility to use linear regression models (proxies of the more complex problem) to predict KPIs with the uncertainty input in seconds, this has a potential use in optimisation problems and probabilistic exercises like Monte Carlo simulation (MC).

As an example a numerical simulation model could take 2 hours to run, if we assume capacity to run 10 concurrent scenarios, a 10000 MC simulation would take around 2000 hours or over 80 days. An ED sample set of 100 numerical simulation cases would take 20 hours, and a proxy generated out of the regression process would take seconds to run. More over the MC simulation can be repeated and the proxy improved with space filling designs (adding few more numerical simulation cases).


Figure 3 : MC simulation for NPV at $100/bbl along with P10,P50 and P90 points. 
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