Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives

Artificial neural networks have been widely applied in reservoir engineering. As a powerful tool, it changes the way to find solutions in reservoir simulation profoundly. Deep learning networks exhibit robust learning capabilities, enabling them not only to detect patterns in data, but also uncover underlying physical principles, incorporate prior knowledge of physics, and solve complex partial differential equations. This work presents the latest research advancements in the field of petroleum reservoir engineering, covering three key research directions based on artificial neural networks: data-driven methods, physics driven artificial neural network partial differential equation solver, and data and physics jointly driven methods. In addition, a wide range of neural network architectures are reviewed, including fully connected neural networks, convolutional neural networks, recurrent neural networks, and so on. The basic principles of these methods and their limitations in practical applications are also outlined. The future trends of artificial intelligence methods for oil and gas reservoir development are further discussed. The large language models are the most advanced neural networks so far, it is expected to be applied in reservoir simulation to predict the development performance.Document Type: PerspectiveCited as: Xue, L., Li, D., Dou, H. Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives. Advances in Geo-Energy Research, 2023, 10(1): 65-70. https://doi.org/10.46690/ager.2023.10.0

A study of correlation between permeability and pore space based on dilation operation

CO2 and fracturing liquid injection into tight and shale gas reservoirs induces reactivity between minerals and injected materials, which results in porosity change and thus permeability change. In this paper, the dilation operation is used to simulate the change of the porosity and the corresponding change of permeability based on Lattice-Boltzmann is studied. Firstly we obtain digital images of a real core from CT experiment. Secondly the pore space of digital cores is expanded by dilation operation which is one of basic mathematical morphologies. Thirdly, the distribution of pore bodies and pore throats is obtained from the pore network modeling extracted by maximal ball method. Finally, the correlation between network modeling parameters and permeabilities is analyzed. The result is that the throat change leads to exponential change of permeability and that the big throats significantly influence permeability.Cited as: Zha, W., Yan, S., Li, D., et al. A study of correlation between permeability and pore space based on dilation operation. Advances in Geo-Energy Research, 2017, 1(2): 93-99, doi: 10.26804/ager.2017.02.0

Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty

 Generative Adversarial Networks (GANs), as most popular artificial intelligence models in the current image generation field, have excellent image generation capabilities. Based on Wasserstein GANs with gradient penalty, this paper proposes a novel digital core reconstruction method. First, a convolutional neural network is used as a generative network to learn the distribution of real shale samples, and then a convolutional neural network is constructed as a discriminative network to distinguish reconstructed shale samples from real ones. Through this confrontation training method, realistic digital core samples of shale can be reconstructed. The paper uses two-point covariance function, Frechet Inception Distance and Kernel Inception Distance, to evaluate the quality of digital core samples of shale reconstructed by GANs. The results show that the covariance function can test the similarity between generated and real shale samples, and that GANs can efficiently reconstruct digital core samples of shale with high-quality. Compared with multiple point statistics, the new method does not require prior inference of the probability distribution of the training data, and directly uses noise vector to generate digital core samples of shale without using constraints of "hard data" in advance. It is easy to produce an unlimited number of new samples. Furthermore, the training time is also shorter, only 4 hours in this paper. Therefore, the new method has some good points compared with current methods.Cited as: Zha, W., Li, X., Xing, Y., He, L., Li, D. Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty. Advances in Geo-Energy Research, 2020, 4(1): 107-114, doi: 10.26804/ager.2020.01.1

Flat-Cladding Fiber Bragg Grating Sensors for Large Strain Amplitude Fatigue Tests

We have successfully developed a flat-cladding fiber Bragg grating sensor for large cyclic strain amplitude tests of up to ±8,000 με. The increased contact area between the flat-cladding fiber and substrate, together with the application of a new bonding process, has significantly increased the bonding strength. In the push-pull fatigue tests of an aluminum alloy, the plastic strain amplitudes measured by three optical fiber sensors differ only by 0.43% at a cyclic strain amplitude of ±7,000 με and 1.9% at a cyclic strain amplitude of ±8,000 με. We also applied the sensor on an extruded magnesium alloy for evaluating the peculiar asymmetric hysteresis loops. The results obtained were in good agreement with those measured from the extensometer, a further validation of the sensor

Application of the ensemble Kalman filter for assisted layered history matching

Ensemble Kalman filter (EnKF) method has been used for automatic history matching the well production data such as production rate and watercut. However, the data of the connection watercut and connection rate are rarely used. In this work we conducted a history matching study based on the connection information using the EnKF for the first time to improve the matching accuracy. First, the initial implementation models are generated using the sequential Gaussian simulation method. Second, we choose the well watercut and connection watercut of each layer as production data respectively. During this step, the data such as permeability, pressure, saturation, and production data are normalized to improve the accuracy of history matching and reduce the simulation time. Finally, the case using the well watercut as historical production data is compared against the case using the connection watercut using EnKF. The results show that the well bottomhole pressure and connection watercut can be better matched using the connection watercut as the historical production data. In addition, the simulation time decreases significantly.Cited as: Zha, W., Gao, S., Li, D., Chen, K. Application of the ensemble Kalman filter for assisted layered history matching. Advances in Geo-Energy Research, 2018, 2(4): 450-456, doi: 10.26804/ager.2018.04.0

An equivalent single-phase flow for oil-water two-phase flow and its potential application in well test

In this work an equivalent single-phase flow model is proposed based on the oil-water two-phase flow equation with saturation-dependent parameters such as equivalent viscosity and equivalent formation volume factor. The equivalent viscosity is calculated from the oil-water relative permeability curves and oil-water viscosity. The equivalent formation volume factor is obtained by the fractional flow of the water phase. In the equivalent single-phase flow model, the equivalent viscosity and phase saturation are interdependent when the relative permeability curves are known. Four numerical experiments based on PEBI grids show that equivalent single-phase flow has a good agreement with the oil-water two-phase flow, which shows that the equivalent single-phase flow model can be used to interpret oil-water two-phase pressure data measured in the wellbore during the buildup period. Because numerical solution of single-phase flow model is several times faster than that of the two-phase flow model, whether the new model interprets the pressure data directly or offers good initial values for the true oil-water two-phase pressure data interpretation, it will obviously improve the efficiency of the interpretation of oil-water pressure data and decrease the burden of engineers.Cited as: Zha, W., Li, D., Lu, Z., Jia, B. An equivalent single-phase flow for oil-water two-phase flow and its potential application in well test. Advances in Geo-Energy Research, 2018, 2(2): 218-227, doi: 10.26804/ager.2018.02.0

Transcriptome profiling reveals the role of ZBTB38 knock-down in human neuroblastoma

ZBTB38 belongs to the zinc finger protein family and contains the typical BTB domains. As a transcription factor, ZBTB38 is involved in cell regulation, proliferation and apoptosis, whereas, functional deficiency of ZBTB38 induces the human neuroblastoma (NB) cell death potentially. To have some insight into the role of ZBTB38 in NB development, high throughput RNA sequencing was performed using the human NB cell line SH-SY5Y with the deletion of ZBTB38. In the present study, 2,438 differentially expressed genes (DEGs) in ZBTB38−/− SH-SY5Y cells were obtained, 83.5% of which was down-regulated. Functional annotation of the DEGs in the Kyoto Encyclopedia of Genes and Genomes database revealed that most of the identified genes were enriched in the neurotrophin TRK receptor signaling pathway, including PI3K/Akt and MAPK signaling pathway. we also observed that ZBTB38 affects expression of CDK4/6, Cyclin E, MDM2, ATM, ATR, PTEN, Gadd45, and PIGs in the p53 signaling pathway. In addition, ZBTB38 knockdown significantly suppresses the expression of autophagy-related key genes including PIK3C2A and RB1CC1. The present meeting provides evidence to molecular mechanism of ZBTB38 modulating NB development and targeted anti-tumor therapies

Pressure-Transient Behavior in a Multilayered Polymer Flooding Reservoir

A new well-test model is presented for unsteady flow in multizone with crossflow layers in non-Newtonian polymer flooding reservoir by utilizing the supposition of semipermeable wall and combining it with the first approximation of layered stable flow rates, and the effects of wellbore storage and skin were considered in this model and proposed the analytical solutions in Laplace space for the cases of infinite-acting and bounded systems. Finally, the stable layer flow rates are provided for commingled system and crossflow system in late-time radial flow periods

Pressure-Transient Behavior in a Multilayered Polymer Flooding Reservoir

A new well-test model is presented for unsteady flow in multizone with crossflow layers in non-Newtonian polymer flooding reservoir by utilizing the supposition of semipermeable wall and combining it with the first approximation of layered stable flow rates, and the effects of wellbore storage and skin were considered in this model and proposed the analytical solutions in Laplace space for the cases of infinite-acting and bounded systems. Finally, the stable layer flow rates are provided for commingled system and crossflow system in late-time radial flow periods