Cause analysis and solutions of water blocking damage in cracked/non-cracked tight sandstone gas reservoirs

Wang, Jie; Zhou, Fujian

doi:10.1007/s12182-020-00482-6

Cited by 29 publications

(11 citation statements)

References 27 publications

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“…It can be seen that the reservoir formation porosity and pore throat radius varied greatly at different depths, but that the matrix was relatively tight in general, and that the pore size distribution contributing to the permeability was mainly 1.9–0.16 μm. Therefore, potential water blocking damage might exist [ 32 ].…”

Section: Resultsmentioning

confidence: 99%

Formation-Damage Mechanism and Gel-Breaker-Free Drill-In Fluid for Carbonate Reservoir

Fang

Zhao

Sun

et al. 2022

Gels

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Abundant oil and gas reserves have been proved in carbonates, but formation damage affects their production. In this study, the characteristics and formation-damage mechanism of the carbonate reservoir formation of the MS Oilfield in the Middle East were analyzed—utilizing X-ray diffraction, a scanning electron microscope, slice identification, and mercury intrusion—and technical measures for preventing formation damage were proposed. An ‘improved ideal filling for temporary plugging’ theory was introduced, to design the particle size distribution of acid-soluble temporary plugging agents; a water-based drill-in fluid, which did not require gel-breaker treatment, was formed, and the properties of the drill-in fluid were tested. The results showed that the overall porosity and permeability of the carbonate reservoir formation were low, and that there was a potential for water-blocking damage. There were micro-fractures with a width of 80–120 μm in the formation, which provided channels for drill-in fluid invasion. The average content of dolomite is 90.25%, and precipitation may occur under alkaline conditions. The polymeric drill-in fluid had good rheological and filtration properties, and the removal rate of the filter cake reached 78.1% in the chelating acid completion fluid without using gel breakers. In the permeability plugging test, the drill-in fluid formed a tight plugging zone on the surface of the ceramic disc with a pore size up to 120 μm, and mitigated the fluid loss. In core flow tests, the drill-in fluid also effectively plugged the formation core samples by forming a thin plugging layer, which could be removed by the chelating acid completion fluid, indicated by return permeability higher than 80%. The results indicated that the drill-in fluid could mitigate formation damage without the treatment of gel breakers, thus improving the operating efficiency and safety.

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Section: Resultsmentioning

confidence: 99%

Formation-Damage Mechanism and Gel-Breaker-Free Drill-In Fluid for Carbonate Reservoir

Fang

Zhao

Sun

et al. 2022

Gels

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“…In 2020, based on the study of reservoir geological characteristics, Dongyu et al [14] used laboratory experiments to determine the reservoir damage mechanism during water injection and believed that strong water sensitivity and high water injection intensity were the main damages in the development process. In the same year, Wang and Zhou [15] used electron microscope scanning technology to study the difference in water plugging damage between uncracked cores and cracked cores and evaluated the improvement effect of the new nanoemulsion on microclogging and liquid flow capacity. In 2021, Zhu et al [16] reviewed the application research progress of NMR technology in the role of polymer gels, showing that it has broad application prospects in evaluating the performance of gel and monitoring the improvement effect of gel.…”

Section: Introductionmentioning

confidence: 99%

Study on Damage Mechanism of Waterflooding Development in Weizhou 11-4N Low-Permeability Oilfield

Zhang

Zhu

Feng³

et al. 2023

Geofluids

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Weizhou 11-4N oilfield is a medium-low-porosity and low-permeability reservoir. The oilfield was initially developed by edge and bottom water energy and then transferred to water injection development. Affected by poor physical properties and heterogeneity of the reservoir, the oilfield appeared in the process of water injection development. When the water injection pressure increases, the water injection volume continues to decrease, and it is difficult to meet the injection requirements. On the basis of the analysis of reservoir heterogeneity, void structure, and clay minerals of reservoir, the water injection compatibility experiment, damage evaluation experiment, and nuclear magnetic resonance-velocity sensitivity experiments were carried out to clarify the damage in the process of oilfield water flooding development. Experiments show that the main causes of damage in Weizhou 11-4N oilfield water flooding development process are water quality incompatibility and strong velocity-sensitive damage. The determination of water type shows that the injected water and formation water are MgCl2 water type and NaHCO3 water type, respectively, under the classification of Surin water type, resulting in the formation of scale with calcium carbonate as the main component in the reservoir. Incompatibility of water quality is an important cause of reservoir damage and scaling. In the reservoir-sensitive flow experiment, the experimental core showed strong velocity sensitivity, the average velocity sensitivity damage rate was 466.31%, and the average critical velocity was 2.98 m/d. Nuclear magnetic resonance experiments show that the core has a significant decrease in average pore size after water flooding. The main damage range is the tiny throat of 0-2 μm. In this paper, the main damage interval of velocity-sensitive damage in the Weizhou 11-4N area and the change trend of void structure after velocity-sensitive experiment are clarified by nuclear magnetic resonance and velocity sensitivity experiments. The main cause of block reservoir damage provides the basis for the oilfield to take targeted measures and provides a guarantee for the efficient development of the subsequent oilfield.

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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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Cause analysis and solutions of water blocking damage in cracked/non-cracked tight sandstone gas reservoirs

Cited by 29 publications

References 27 publications

Formation-Damage Mechanism and Gel-Breaker-Free Drill-In Fluid for Carbonate Reservoir

Formation-Damage Mechanism and Gel-Breaker-Free Drill-In Fluid for Carbonate Reservoir

Study on Damage Mechanism of Waterflooding Development in Weizhou 11-4N Low-Permeability Oilfield

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

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