Development of universal proxy models for screening and optimization of cyclic pressure pulsing in naturally fractured reservoirs

Artun, Emre; Ertekin, Turgay; Watson, R.W.; Al-Wadhahi, Majid

doi:10.1016/j.jngse.2011.07.016

Cited by 23 publications

(3 citation statements)

References 40 publications

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“…Ayala and Ertekin (2007) and Ayala et al (2007) built ANNs to analyze gas-cycling performance and studied development in gas/condensate reservoirs. There are numerous further uses (Alajmi and Ertekin, 2007; Artun et al, 2011a, 2011b; Bansal et al, 2013; Enab and Ertekin, 2014; Enyioha and Ertekin, 2014; Parada and Ertekin, 2012; Ramgulam et al, 2007; Siripatrachai et al, 2014; Sun and Ertekin, 2012, 2017, 2018). Such work also includes Masoudi et al (2012) and Masoudi et al (2014).…”

Section: Methodsmentioning

confidence: 99%

An artificial-intelligence-based petrophysical property predictor for compositional volatile oil reservoir using three-phase production data

Zhang,

Ertekin,

et al. 2024

Energy Exploration & Exploitation

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When considering multiphase flow scenarios, the interpretation of petrophysical properties poses significant challenges for production forecasts and reservoir modeling. The findings of the numerical modeling were therefore subject to uncertainty because characteristics like relative permeability and capillary pressure curve were hardly ever bound by interpretations. The uncertainty may result in inaccurate predictions of reservoir performance and skewed perceptions of the reservoir. Due to the difficulty in directly interpreting such property from the available field data and the expensive cost of coring, analyses or experimental measurements to determine relative permeability and capillary pressure were infrequently carried out. Such a gap would be filled by a straightforward yet rigorous method. In this study, we develop production projections for a wide range of three-phase compositional volatile oil reservoirs. Then, we used an artificial neural network to figure out how petrophysical characteristics and production data relate to one another. The artificial neural network model was adjusted, and the final trained model was tested blindly to determine how well it predicted permeability, multiphase relative permeability, and capillary pressure data. For the testing scenarios, consistency is seen between the predicted values and the original ones, despite some mispredictions being present. To provide production projections that can be compared to those from the reservoir model that include the initial petrophysical characteristic, the anticipated properties are then propagated into reservoir models. The comparison findings show that for 65/59/34 out of 74 testing scenarios, the reservoir model with artificial neural network-predicted features can anticipate oil/gas/water output with < 20% inaccuracy. With the developed artificial neural network tool, the reservoir engineers can evaluate the three-phase relative permeability surface from rate-transient data conveniently improving the accuracy of the relative permeability data implemented by history matching or from core experiments which sometimes are extremely expensive. The findings of this study can help for a better understanding of the relationships between three-phase rate-transient data and the relative permeability surface as well as the horizontal/vertical permeability.

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Section: Methodsmentioning

confidence: 99%

An artificial-intelligence-based petrophysical property predictor for compositional volatile oil reservoir using three-phase production data

Zhang,

Ertekin,

et al. 2024

Energy Exploration & Exploitation

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“…In the literature, a wide variety of techniques can be considered as TPMs. This type of proxy can approximate different areas in the subsurface or surface environment such as production optimization [100,167], uncertainty quantification [168,169], history matching [170,171], field development planning [172], risk analysis [173,174], gas lift optimization [109,175], gas storage management [176], screening purposes in fractured reservoirs [177], hydraulic fracturing [178], assessing the petrophysical and geomechanical properties of shale reservoirs [179], waterflooding optimization [180][181][182][183], well placement optimization [184][185][186], wellhead data interpretation [187], and well control optimization [188]. Additionally, TPMs have a wide range of applications in various EOR recovery techniques such as steam-assisted gravity drainage (SAGD) [189], CO 2 -gas-assisted gravity drainage (GAGD) [190], water alternating gas (WAG) [191,192], and chemical flooding [193].…”

Section: Traditional Proxy Models (Tpm)mentioning

confidence: 99%

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

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Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.

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“…Artificial neural networks (ANN) are very powerful in extracting non-linear and complex relationships between input and output patterns. Several areas of application included reservoir characterization (Artun and Mohaghegh 2011;Raeesi et al 2012;Alizadeh et al 2012;Artun 2016), candidate well selection for hydraulic fracturing treatments (Mohaghegh et al 1996), field development (Centilmen et al 1999;Doraisamy et al 2000;Mohaghegh et al 1996), well-placement and trajectory optimization Rogers 2011, Guyaguler 2002;Yeten et al 2003), scheduling of cyclic steam injection processes (Patel et al 2005), screening and optimization of secondary/enhanced oil recovery (Ayala and Ertekin 2005;Artun et al 2010Artun et al , 2011bArtun et al , 2012Parada and Ertekin 2012;Amirian et al 2013), history matching (Cullick et al 2006;Silva et al 2007;Zhao et al 2015), underground-gas-storage management (Zangl et al 2006), reservoir monitoring and management (Zhao et al 2015;Mohaghegh et al 2014), and modeling of shale-gas reservoirs (Kalantari-Dhaghi et al 2015;Esmaili and Mohaghegh 2015).…”

Section: Development Of a Screening Toolmentioning

confidence: 99%

Modeling, analysis, and screening of cyclic pressure pulsing with nitrogen in hydraulically fractured wells

2016

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Cyclic pressure pulsing with nitrogen is studied for hydraulically fractured wells in depleted reservoirs. A compositional simulation model is constructed to represent the hydraulic fractures through local-grid refinement. The process is analyzed from both operational and reservoir/ hydraulic-fracture perspectives. Key sensitivity parameters for the operational component are chosen as the injection rate, lengths of injection and soaking periods and the economic rate limit to shut-in the well. For the reservoir/ hydraulic fracturing components, reservoir permeability, hydraulic fracture permeability, effective thickness and half-length are used. These parameters are varied at five levels. A full-factorial experimental design is utilized to run 1250 cases. The study shows that within the ranges studied, the gas-injection process is applied successfully for a 20-year project period with net present values based on the incremental recoveries greater than zero. It is observed that the cycle rate limit, injection and soaking periods must be optimized to maximize the efficiency. The simulation results are used to develop a neural network based proxy model that can be used as a screening tool for the process. The proxy model is validated with blind-cases with a correlation coefficient of 0.96.

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Development of universal proxy models for screening and optimization of cyclic pressure pulsing in naturally fractured reservoirs

Cited by 23 publications

References 40 publications

An artificial-intelligence-based petrophysical property predictor for compositional volatile oil reservoir using three-phase production data

An artificial-intelligence-based petrophysical property predictor for compositional volatile oil reservoir using three-phase production data

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Modeling, analysis, and screening of cyclic pressure pulsing with nitrogen in hydraulically fractured wells

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