Tight gas production model considering TPG as a function of pore pressure, permeability and water saturation

Zafar, Atif; Su, Yuliang; Li, Lei; Fu, Jingang; Mehmood, Asif; Ouyang, Weiping; Zhang, Mian

doi:10.1007/s12182-020-00430-4

Cited by 19 publications

(12 citation statements)

References 47 publications

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“…Tight sandstone gas reservoirs have huge gas reserves and very significant potential for development. − However, these reservoirs have low or ultra-low porosity/permeability, high water saturation, strong heterogeneity, and complex gas–water seepage (flow of gas–water in rock pore throats). − The water film in the complex and fine pore throats of tight gas reservoir rocks produces greater resistance to seepage. − The gas in the pore throats needs to break through, overcoming the capillary resistance to flow from the static state . Consequently, a certain displacement differential pressure is required initially to counteract this resistance, to start and then to maintain the gas flow along the pore throat path.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Prediction of Production Loss in Heterogeneous Tight Gas Reservoirs Using Dynamic Threshold Pressure

et al. 2022

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Tight gas reservoirs commonly exhibit complex pore throat structures. Conventionally, a constant threshold pressure gradient (TPG) has been used to predict the production loss that arises from overcoming the rock's disinclination to flow water and gas through such complex pore throat structures. In this work, we find that the TPG is not constant during the production lifetime of a reservoir. The TPG varies significantly with both effective stress and water saturation, leading us to rename TPG as dynamic threshold pressure gradient (DTPG). In the first part of this paper, we examine the sensitivity of DTPG to stress and mobile water saturation for cores with different permeabilities, showing that DTPG increases logarithmically with effective stress from 0.17 to 0.5 MPa/m for a change in effective stress from 0.6 to 30.5 MPa. The DTPG also increases exponentially with mobile water saturation (S m ), which is 2.7−6.5 times higher at S m = 25% compared to the value at irreducible water saturation. The sensitivity of DTPG to both variables shows a decreasing power law trend with increasing rock permeability. These combined effects generally suggest that DTPG is larger than the conventional TPG. In the second part of this paper, we model the effects of using a variable DTPG in place of a constant TPG for the purpose of predicting the production loss associated with the latent pressure barrier in different heterogeneous reservoirs. When the interacting effects of effective stress, water saturation, and permeability are taken into account, we find that the threshold pressure is relatively small in heterogeneous reservoirs with a distribution of increasing permeability in the gas flow direction. The constant TPG approach underestimates the production loss by 34−45% with the greatest difference occurring at low gas pressures encountered at the production well, suggesting that gas production wells should be located in areas with high permeability.

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Section: Introductionmentioning

confidence: 99%

“…The permeability of the rock is related to the complexity of the pore throat structure . Consequently, rock permeability is considered to have a significant effect on threshold pressure in tight reservoirs . Moreover, the differential pressure for gas production in tight gas reservoirs is large due to their low permeability.…”

Section: Introductionmentioning

confidence: 99%

Improving the Prediction of Production Loss in Heterogeneous Tight Gas Reservoirs Using Dynamic Threshold Pressure

et al. 2022

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“…However, the flow behavior in a tight gas reservoir is complex. It has been found that such a reservoir has a threshold pressure gradient [9,10]; the permeability in a tight gas reservoir may change with pressure and exhibits a stress-sensitive effect during the production process [11,12]; the proppant embedment issues in the hydraulic fractures of a tight reservoir may affect the gas production [13]; the temperature and pressure may affect the imbibition recovery for tight or shale gas reservoir [14]; the tight gas production may be seriously reduced by water blockage [15][16][17]; the dispersed distribution of kerogen within matrices may affect the production evaluation [18]; sulfur precipitation and reservoir pressure-sensitive effects may affect the permeability, porosity and formation pressure [19]; the micro-scale flow mechanism, such as Knudsen diffusion, slippage effect, and adsorption, can be difficult to describe quantitatively [20,21]. All these complexities associated with tight gas reservoirs make it difficult to build an accurate mathematical model to predict gas flow in tight gas reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models

Gu¹,

Wang²,

Xue³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery, which has an important impact on gas field development planning and economic evaluation. Owing to the model's simplicity, the decline curve analysis method has been widely used to predict production performance. The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs. In this paper, a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed. The sequence learning methods used in production performance analysis herein include the recurrent neural network (RNN), long short-term memory (LSTM) neural network, and gated recurrent unit (GRU) neural network, and their performance in the tight gas reservoir production prediction is investigated and compared. To further improve the performance of the sequence learning method, the hyperparameters in the sequence learning methods are optimized through a particle swarm optimization algorithm, which can greatly simplify the optimization process of the neural network model in an automated manner. Results show that the optimized GRU and RNN models have more compact neural network structures than the LSTM model and that the GRU is more efficiently trained. The predictive performance of LSTM and GRU is similar, and both are better than the RNN and the decline curve analysis model and thus can be used to predict tight gas production.

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“…At present, horizontal well technology combined with segmented multi-stage fracturing technology is commonly used worldwide to achieve efficient development of tight oil reservoirs (Jiang et al, 2017;Zhang, 2020;Guo et al, 2021;Zheng et al, 2021), and the recovery rate can be improved by increasing the stimulated reservoir volume. However, the fracture expansion of tight oil reservoirs is influenced by their own geological conditions with diverse situations (Zhao et al, 2015;Peng et al, 2016;Zhao et al, 2017;Li et al, 2020;Peng et al, 2020), some reservoirs are strongly heterogeneity with positive and reverse rhythm in the vertical direction (Li et al, 2019a;Zhang et al, 2022b), and the two-phase or even three-phase seepage is superimposed on the influence of engineering factors on the development process (Ma et al, 2021;Shen et al, 2022), which makes the seepage mechanism and development law of tight reservoirs very complex (Zafar et al, 2020;Zhao et al, 2021), and the dominant seepage channel is not clear (Li et al, 2019b). At present, the relevant research is still at the core scale (Jing et al, 2021)…”

Section: Introductionmentioning

confidence: 99%

Study on the development options of tight sandstone oil reservoirs and their influencing factors

Huang

et al. 2022

Front. Energy Res.

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The research area of tight sandstone oil reservoirs was selected, a numerical model of the oil reservoir was developed, and a study of the development options and influencing factors was carried out to analyze the influence of different development methods, physical and engineering parameters on the development dynamics. Study shows that the two main factors limiting the efficient development of tight sandstone reservoirs are reservoir properties and formation energy. Fractured horizontal well injection huff and puff development can effectively improve reservoir physical properties and timely replenish formation energy, which is suitable for the development of such oil reservoirs. In dense sandstone reservoirs, its impact on production capacity is also relatively small when the permeability ratio is small. Due to both gravity and reservoir physical properties, the permeability ratio increases, the cumulative oil production of positive rhythm reservoirs decreases and that of reverse rhythm reservoirs increases, and the location of high-quality reservoirs in the upper part of producing wells is conducive to increasing the final recovery rate. A lower oil to water viscosity ratio can significantly increase the swept volume and improve development effect. Hydrophilic reservoirs can reduce the injection pressure and increase the spread range, effectively improving the problem of inability to inject, and improving reservoir hydrophilicity through surface activators can increase reservoir recovery. The water injection rate determines the recovery rate of formation energy. Generally, the faster the rate, the higher the cumulative oil production. Therefore, the rate of water injection should be increased as much as possible, taking into account construction conditions and economic evaluation. Additionally, the effect of water injection on the development effect is different at different stages, so the appropriate timing of water injection is very important to the water injection huff and puff development effect, and the use of early water injection in this research area is not conducive. Soaking can promote pressure and fluid redistribution and improve water injection huff and puff development effect, but soaking for a long time can lead to reservoir contamination and reduce crude oil production, so the preferred time for a soaking is about 20 days.

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Tight gas production model considering TPG as a function of pore pressure, permeability and water saturation

Cited by 19 publications

References 47 publications

Improving the Prediction of Production Loss in Heterogeneous Tight Gas Reservoirs Using Dynamic Threshold Pressure

Improving the Prediction of Production Loss in Heterogeneous Tight Gas Reservoirs Using Dynamic Threshold Pressure

Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models

Study on the development options of tight sandstone oil reservoirs and their influencing factors

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