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The dew point pressure can be obtained by conventional condensate gas phase test in PVT cylinders. However, in condensate gas reservoirs, the fluid interacts with the fractured-vuggy porous media, which makes the depletion process of condensate gas in pores, fractures or vugs different from that in PVT cylinder. In order to study the mechanism of this difference, three different experimental models were made to carry out the condensate gas depletion production experiment. The results showed that the existence of fracture-vuggy porous media would affect the depletion production process. The pores, fractures and vugs increased the dew point pressure from 26.2 MPa to 26.6 MPa and decreased the condensate oil recovery from 72.8% to 70.7%. When the fractures, vugs and pores are filled with powered fine sand of carbonate rocks, the dew point pressure reaches 27.4 MPa and the condensate oil recovery reduces to 68.4%. The fractured-vuggy porous media will aggravate the retro condensation phenomenon, promote the accumulation of heavy hydrocarbon components on the pore surface and precipitate in the form of condensate oil and hinder the recovery of condensate oil. This work explains the mechanism of the influence of fractured-vuggy porous media on the phase behavior of condensate gas. [ABSTRACT FROM AUTHOR]

For fault-controlled fractured-vuggy reservoirs, the development characteristics of bottom water flooding and water and gas injection flooding under multi-well conditions must be clarified due to the structural complexity. To address this issue, we designed and manufactured fault-controlled physical experimental models based on the geological model of Tarim Oilfield and conducted flooding experiments. The results demonstrate significant variations in bottom water flooding characteristics due to differences in flow capacity within fractures, cavity area in caves, and filled area in caves under different filling modes. Different bottom water rates exhibit varying abilities to overcome gravity and breakthrough capillary resistance, significantly impacting the bottom water flooding characteristics. During the bottom water flooding period, the positioning of production wells primarily affects the macroscopic sweep range, while the filling modes significantly influence the distribution of remaining oil within individual caves. Throughout the three periods of multi-well water and gas injection, the early water injection stage mainly focuses on mobilizing "insufficiently controlled remaining oil" and some "attic remaining oil," and the middle gas injection stage primarily targets the attic remaining oil. Finally, the late water injection stage aims to lift the oil–gas–water interface and improve oil displacement efficiency. Furthermore, different water and gas flooding directions affect displacement resistance in each channel and the longitudinal sweep range. This paper identifies the direction for the life cycle production of fault-controlled fractured-vuggy reservoirs and presents a mechanistic explanation. [ABSTRACT FROM AUTHOR]

Waterflooding reservoir interwell connectivity characterization is the fundamental work in oil development, aiming to inverse the vital connecting channels between injectors and producers. In this paper, we endow an artificial neural network (ANN) with strong interpretability through the ordinary differential equation (ODE) of the material balance equation, proposing a physical knowledge fusion neural network (PKFNN). In addition, the proposed model could inherit the knowledge learned from different injector–producer pairs, fully improving the training efficiency. In this way, PKFNN combines the merits of both physical and machine learning approaches. Firstly, based on the physical control law and the ODE of the material balance equation, we endow the model with highly transparent modular architectures in the framework of feedforward neural network. In this way, our work has both high interpretability and excellent approximation ability, combining the merits of the physical and machine learning approaches. The proposed model shows great performance on productivity forecast and interwell connectivity reflection in several reservoir experiments. PKFNN provides a novel way to enhance the interpretability and robustness of the data-driven-based interwell connectivity-analyzing models by integrating the physical knowledge of waterflooding reservoirs. [ABSTRACT FROM AUTHOR]

Due to the complex connectivity of fractured-vuggy reservoirs, there is always a large amount of remaining oil after water flooding development. The blind cave remaining oil, one of the primary remaining oil occurrence forms, is the most difficult to recover. Based on the distribution pattern of these types of remaining oil, the paper designed and manufactured a variety of tube-cave models. Through physical experiment, numerical experiment and theoretical calculation, the ability for water to displace blind cave remaining oil in different conditions, oil-water convective displacement characteristics and the critical parameters for remaining oil development were studied, respectively. The results show that the interfacial tension, fracture width and oil-water gravity difference are the most important parameters to determine whether the remaining oil can be exploited; the wall wettability and oil viscosity mainly affect the oil-water displacement rate due to the loss of non-wetting internal friction. In addition, the interfacial tension should be reduced to 1 mN/m or lower to accelerate the convective flow in the fractures, so that most of the blind cave remaining oil can be developed. This paper provides the influencing factors and exploitation measures of the blind cave remaining oil in fractured-vuggy reservoirs. [ABSTRACT FROM AUTHOR]

The reservoir characterization aims to provide the analysis and quantification of the injection-production relationship, which is the fundamental work for production management. The connectivity between injectors and producers is dominated by geological properties, especially permeability. However, the permeability parameters are very heterogenous in oil reservoirs, and expensive to collect by well logging. The commercial simulators enable to get accurate simulation but require sufficient geological properties and consume excessive computation resources. In contrast, the data-driven models (physical models and machine learning models) are developed on the observed dynamic data, such as the rate and pressure data of the injectors and producers, constructing the connectivity relationship and forecasting the productivity by a series of nonlinear mappings or the control of specific physical principles. While, due to the "black box" feature of machine learning approaches, and the constraints and assumptions of physical models, the data-driven methods often face the challenges of poor interpretability and generalizability and the limited application scopes. To solve these issues, integrating the physical principle of the waterflooding process (material balance equation) with an artificial neural network (ANN), a knowledge interaction neural network (KINN) is proposed. KINN consists of three transparent modules with explicit physical significance, and different modules are joined together via the material balance equation and work cooperatively to approximate the waterflooding process. In addition, a gate function is proposed to distinguish the dominant flowing channels from weak connecting ones by their sparsity, and thus the inter-well connectivity can be indicated directly by the model parameters. Combining the strong nonlinear mapping ability with the guidance of physical knowledge, the interpretability of KINN is fully enhanced, and the prediction accuracy on the well productivity is improved. The effectiveness of KINN is proved by comparing its performance with the canonical ANN, on the inter-well connectivity analysis and productivity forecast tasks of three synthetic reservoir experiments. Meanwhile, the robustness of KINN is revealed by the sensitivity analysis on measurement noises and wells shut-in cases. [ABSTRACT FROM AUTHOR]

Attic karst cave is one of the most common fractured-vuggy structure in Tahe oilfield. In this study, a typical attic-shaped cave was extracted from the geological section of Tahe Oilfield. Under the guidance of similarity criteria, physical experiment models of attic karst cave reservoir filled with oil-wet and water-wet filling medium were designed and fabricated using coated resin sand and cemented quartz sand. Water flooding experiments at different injection rates were carried out using the attic karst cave model filled with filling medium of various wettability. The water flooding characteristics and distribution law of residual oil in the different models were determined by analyzing oil displacement performances and the dynamic production curves. Results showed that when the filling medium is water-wet, water injection rate has a trivial impact on the residual oil distribution pattern. While for the oil-wet media filled situations, the lower the injection rate, the lower the swept area. However, at high injection rates, the spread scope is large but the displacement efficiency remains relatively low. In addition, it was noted that the difference in the wettability of the filling medium caused a difference in the location of the residual oil. [ABSTRACT FROM AUTHOR]