Analytical dual-porosity gas model for reserve evaluation of naturally fractured gas reservoirs using a density-based approach

Zhang, Zhenzihao; Ayala, Luis F.

doi:10.1016/j.jngse.2018.08.022

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…( 11) is valid for gas BDF, regardless of whether the production scenario is under constant or variable BHP conditions. Zhang et al (2018) compared the numerical production results and the production for 118 different dual-porosity models, and the exact match in BDF for all cases were obtained. However, Eq.…”

Section: Methodsmentioning

confidence: 99%

Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis

Wang,

Zhao

et al. 2023

Adv. Geo-Energy Res.

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Section: Methodsmentioning

confidence: 99%

Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis

Wang,

Zhao

et al. 2023

Adv. Geo-Energy Res.

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“…In this study, our goal is to use production data to assess permeability, capillary pressure curves, and three-phase relative permeability properties. The evaluation of reserve and hydraulic fracture qualities has mostly relied on production data (Blasingame et al, 1991; Fetkovich, 1980; Wattenbarger et al, 1998; Zhang and Ayala H., 2016, 2017, 2018). Using proxies, Zhang and Ertekin (2021) evaluated the petrophysical characteristics of black oil reservoirs.…”

Section: Introductionmentioning

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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“…Production data have long been used for interpretation of reservoir properties such as permeability. 42–47 However, it has never been used to evaluate three-phase RP to the best of our knowledge. An ANN is an effective tool for simulating relationships.…”

Section: Introductionmentioning

confidence: 99%

Proxy models for evaluation of permeability, three-phase relative permeability, and capillary pressure curves from rate-transient data

Zhang

Ertekin

2019

SIMULATION

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This study developed a data-driven forecasting tool that predicts petrophysical properties from rate-transient data. Traditional estimations of petrophysical properties, such as relative permeability (RP) and capillary pressure (CP), strongly rely on coring and laboratory measurements. Coring and laboratory measurements are typically conducted only in a small fraction of wells. To contend with this constraint, in this study, we develop artificial neural network (ANN)-based tools that predict the three-phase RP relationship, CP relationship, and formation permeability in the horizontal and vertical directions using the production rate and pressure data for black-oil reservoirs. Petrophysical properties are related to rate-transient data as they govern the fluid flow in oil/gas reservoirs. An ANN has been proven capable of mimicking any functional relationship with a finite number of discontinuities. To generate an ANN representing the functional relationship between rate-transient data and petrophysical properties, an ANN structure pool is first generated and trained. Cases covering a wide spectrum of properties are then generated and put into training. Training of ANNs in the pool and comparisons among their performance yield the desired ANN structure that performs the most effectively among the ANNs in the pool. The developed tool is validated with blind tests and a synthetic field case. Reasonable predictions for the field cases are obtained. Within a fraction of second, the developed ANNs infer accurate characteristics of RP and CP for three phases as well as residual saturation, critical gas saturation, connate water saturation, and horizontal permeability with a small margin of error. The predicted RP and CP relationship can be generated and applied in history matching and reservoir modeling. Moreover, this tool can spare coring expenses and prolonged experiments in most of the field analysis. The developed ANNs predict the characteristics of three-phase RP and CP data, connate water saturation, residual oil saturation, and critical gas saturation using rate-transient data. For cases fulfilling the requirement of the tool, the proposed technique improves reservoir description while reducing expenses and time associated with coring and laboratory experiments at the same time.

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Analytical dual-porosity gas model for reserve evaluation of naturally fractured gas reservoirs using a density-based approach

Cited by 9 publications

References 20 publications

Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis

Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis

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

Proxy models for evaluation of permeability, three-phase relative permeability, and capillary pressure curves from rate-transient data

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