In the petroleum industry, artificial intelligence has been applied in seismic and logging interpretation, accurate modeling, optimized drilling operations, well dynamics prediction, safety warning, etc. However, field-scale application and deployment remain a challenge due to the lack of sufficiently powerful algorithms for the integration of multi-granularity data in the time and space domain, for the construction of a deep-learning network able to represent the evolution of well and reservoir dynamics, and finally the lack of investment in surveillance data acquisition. This paper offers a concise review of smart field deployment for mature waterflood reservoirs, including the current status of data foundation construction, and the research progress for applied AI algorithms, as well as application scenarios and overall deployment. With respect to data, the domestic and international oil and gas industry has completed or at least started the large-scale construction and deployment of lake data. However, the data isolation phenomenon is serious in China. Preparation for the integration of new monitoring data for the overall research of reservoirs is insufficient. With respect to algorithms, data-based and model-based AI algorithms have been emerging recently, but the development of the overall proxy model for rapid prediction and automatic model calibration is still in the preliminary period. For application scenarios, relatively simple and independent applications related to geophysical interpretation and production engineering are continuing to emerge, while large-scale reservoir and field application require substantial investment in data acquisition, game-changing algorithms with cloud-based computing architecture, and top-down deployment.

During the development of oilfields, casings in long-term service tend to be damaged to different degrees, leading to poor development of the oilfields, ineffective water circulation, and wasted water resources. In this paper, we propose a data-based method for predicting casing failure risk at both well and well-layer granularity and illustrate the application of the method to GX Block in an eastern oilfield of China. We first quantify the main control factors of casing damage by adopting the F-test and mutual information, such as that of the completion days, oil rate, and wall thickness. We then select the top 30 factors to construct the probability prediction model separately using seven algorithms, namely the decision tree, random forest, AdaBoost, gradient boosting decision tree, XGBoost, LightGBM, and backpropagation neural network algorithms. In terms of five evaluation indicators, namely the accuracy, precision, recall, F1-score, and area under the curve, we find that the LightGBM algorithm yields the best results at both granularities. The accuracy of the prediction model based on the preferred algorithm reaches 87.29% and 92.45% at well and well-layer granularity, respectively.

Block M of the Ordos Basin is a typical low-permeability tight sandstone gas accumulation. To develop these reservoirs, various horizontal well fracturing technologies, such as hydra-jet fracturing, open-hole packer multistage fracturing, and perf-and-plug multistage fracturing, have been implemented in practice, showing greatly varying performance. In this paper, six fracturing technologies adopted in Block M are reviewed in terms of principle, applicability, advantages, and disadvantages, and their field application effects are compared from the technical and economic perspectives. Furthermore, the main factors affecting the productivity of fractured horizontal wells are determined using the entropy method, the causes for the difference in application effects of the fracturing technologies are analyzed, and a comprehensive productivity impact index (CPII) in good correlation with the single-well production of fractured horizontal wells is constructed. This article provides a simple and applicable method for predicting the performance of multi-frac horizontal wells that takes multiple factors into account. The results can be used to select completion methods and optimize fracturing parameters in similar reservoirs.

Most of the oil reservoirs in China are fluvial deposits with firm reservoir heterogeneity, where differences in fluid flow capacity in individual directions should not be ignored; however, the available commercial reservoir simulation software cannot consider the anisotropy of the relative permeability. To handle this challenge, this paper takes full advantage of the parallelism of the multi-scale finite volume (MsFV) method and establishes a multi-scale numerical simulation approach that incorporates the effects of reservoir anisotropy. The methodology is initiated by constructing an oil–water black-oil model considering the anisotropic relative permeability. Subsequently, the base model undergoes decoupling through a sequential solution, formulating the pressure and transport equations. Following this, a multi-scale grid system is configured, within which the pressure and transport equations are progressively developed in the fine-scale grid domain. Ultimately, the improved multi-scale finite volume (IMsFV) method is applied to mitigate low-frequency error in the coarse-scale grid, thereby enhancing computational efficiency. This paper introduces two primary innovations. The first is the development of a multi-scale solution method for the pressure equation incorporating anisotropic relative permeability. Validated using the Egg model, a comparative analysis with traditional numerical simulations demonstrates a significant improvement in computational speed without sacrificing accuracy. The second innovation involves applying the multi-scale framework to investigate the impact of anisotropy relative permeability on waterflooding performance, uncovering distinct mechanisms by which absolute and relative permeability anisotropy influence waterflooding outcomes. Therefore, the IMsFV method can be used as an effective tool for high-resolution simulation and precise residual oil prediction in anisotropic reservoirs.

Offshore reservoirs in the high water-cut stage present significant development challenges, including declining production, complex remaining oil distribution, and the inadequacy of conventional evaluation methods to capture intricate flow dynamics. To overcome these limitations, this study introduces a novel approach based on flow diagnostics for performance evaluation and potential adjustment. The method integrates key metrics such as time-of-flight (TOF) and the dynamic Lorenz coefficient, supported by reservoir engineering principles and numerical simulation, to construct a multi-parameter evaluation system. This system, which also incorporates injection–production communication volume and inter-well fluid allocation factors, precisely quantifies and visualizes waterflood displacement processes and sweep efficiency. Applied to the QHD32 oilfield, this framework was used to establish specific thresholds for operational adjustments. These include criteria for infill drilling (waterflooded ratio < 45%, remaining oil thickness > 6 m, TOF > 200 days), conformance control (TOF < 50 days, dynamic Lorenz coefficient > 0.5), and artificial lift optimization (remaining oil thickness ratio > 2/3, TOF > 200 days). Field validation confirmed the efficacy of this approach: an additional cumulative oil production of 165,600 m³ was achieved from infill drilling in the C29 well group, while displacement adjustments in the B03 well group increased oil production by 2.2–3.8 tons/day, demonstrating a significant enhancement in waterflooding performance. This research provides a theoretical foundation and a technical pathway for the refined development of offshore heavy oil reservoirs at the ultra-high water-cut stage, offering a robust framework for the sustainable management of analogous reservoirs worldwide.