Productions of volatile oil and gas-condensate from liquid rich shales

Panja, Palash; Pathak, Manas A.; Deo, Milind

doi:10.26804/ager.2019.01.02

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…In the experimental test, the appearance of cracks will greatly change the physical characteristics of the sample Xu et al, 2020b). The clay mineral content in the mixed shales is relatively high, and the shale with high clay mineral content is prone to undercompaction during the diagenetic evolution process (Liu et al, 2019;Panja et al, 2019;Teng et al, 2021). The cementation between layers of clay minerals is relatively weak, with cracks visible (Figures 12A,B).…”

Section: Control Mechanism Of Lacustrine Shale Oil Reservoirmentioning

confidence: 99%

Characteristics and Control Mechanism of Lacustrine Shale Oil Reservoir in the Member 2 of Kongdian Formation in Cangdong Sag, Bohai Bay Basin, China

Zhao²,

Pu³

et al. 2021

Front. Earth Sci.

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The significance of lacustrine shale oil has gradually become prominent. Lacustrine shale has complex lithologies, and their reservoir properties are quite various. The multi-scale pore structure of shale controls the law of shale oil enrichment. Typical lacustrine shale developed in the Member 2 of Kongdian Formation in Cangdong sag, Bohai Bay Basin. The lithofacies and multi-scale storage space of this lacustrine shale have been systematically studied. 1. The mineral composition is quite different, and the lithofacies can be summarized into siliceous, carbonate and mixed types. The rock structure can be summarized into laminated, layered, and massive types. 2. The pores are diverse and multi-scale. Interparticle pores contribute the main storage space, especially the interparticle pores of quartz and dolomite. 3. The physical properties of the massive shales is relatively inferior to those of layered and laminatedtypes, and it presents the characteristics of " laminated >layered > massive ". The developed laminae can significantly improve the space and seepage capacity of lacustrine shale. 4. Clay minerals provide the main nano-scale storage space, but they are often filled in pores and reduces the shale brittleness, which have destruction effects.

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Section: Control Mechanism Of Lacustrine Shale Oil Reservoirmentioning

confidence: 99%

Characteristics and Control Mechanism of Lacustrine Shale Oil Reservoir in the Member 2 of Kongdian Formation in Cangdong Sag, Bohai Bay Basin, China

Zhao²,

Pu³

et al. 2021

Front. Earth Sci.

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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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“…While significant advances are being made in stimulating shale oil reservoirs; however many challenges (especially phase behavior, production mechanism and optimization strategy) in modeling of these reservoir remain (Feng et al, 2020;Taghavinejad et al, 2020). Panja et al (2019) studied the challenges in the production of shale oil and gas-condensate reservoirs. They investigated the effect of the fluid (volatile oil or condensate) and reservoir permeability and operating conditions on ultimate recoveries.…”

Section: Introductionmentioning

confidence: 99%

A workflow for flow simulation in shale oil reservoirs: A case study in woodford shale

Sharifi

Kelkar

Karkevandi-Talkhooncheh

2021

Adv. Geo-Energy Res.

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In recent years, with new technologies of long horizontal wells and staged hydraulic fracturing, the development of unconventional oil and gas reservoirs (i.e., shale gas and shale oil) has gained significant momentum. Due to extremely low permeability, these unconventional formations cannot be produced economically without significant stimulation. In the current research, the workflow for shale reservoir history matching that can be used for other shale resources producing from either condensate or oil reservoirs is developed. Production data and well geometry data for nineteen wells were available in Woodford shale. During this work, using the available data, single well simulation models for all the individual wells were constructed and then models were tuned to match the historical data. It has been shown that the fracture half length, shear fracture distribution and the interaction between matrix and fractures should be captured. Also, the results showed that fracture half-length can be longer than 2,000 ft, but the permeability of the fracture is dependent on how far the fracture is from the well. It was found that for multiple well history matching, fracture half-length and the interaction between the wells are the most important factors. Using multiple history matched models, it was shown that multiple models with different fracture distributions could capture the historical data, but they exhibit different future predictions.

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Productions of volatile oil and gas-condensate from liquid rich shales

Cited by 9 publications

References 17 publications

Characteristics and Control Mechanism of Lacustrine Shale Oil Reservoir in the Member 2 of Kongdian Formation in Cangdong Sag, Bohai Bay Basin, China

Characteristics and Control Mechanism of Lacustrine Shale Oil Reservoir in the Member 2 of Kongdian Formation in Cangdong Sag, Bohai Bay Basin, China

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

A workflow for flow simulation in shale oil reservoirs: A case study in woodford shale

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