In high water-cut reservoirs, water flooding efficiency declines due to high-permeability channels that aggravate heterogeneity and cause water channeling. Gas flooding technology can improve recovery but is limited by operating conditions and costs. Water-alternating-gas (WAG) injection combines the advantages of both methods, but the complex reservoir connectivity and the strong inter-well interference hinder accurate displacement path control, which will reduce the oil production efficiency and increase costs. In this paper, a reinforcement learning-based dynamic optimization strategy is proposed to solve the injection optimization problem for WAG injection with inter-well connectivity. Specifically, a net present value (NPV) model is built for a single time slot based on the oil production, the injection cost of water or gas, the conversion cost between water flooding and gas flooding, and the inter-well connectivity. Furthermore, an NPV optimization problem is formulated for multi-injection wells and multi-production wells to enhance oil recovery. Then, a Q-learning algorithm is adopted for solving the proposed optimization problem to achieve the optimal WAG injection strategy, in which the state space comprises pressure and three-phase saturation, the action space includes injection type and rate, and the reward function is the total recovery benefit. Finally, extensive numerical simulations are conducted. Quantitative results demonstrate that the proposed algorithm achieves a converged NPV of approximately $8.3 million, outperforming the fixed-rate WAG strategy by 3.75%, the water flooding strategy by 9.21%, and the gas flooding strategy by 12.16%. Moreover, the proposed method increases cumulative oil production by 4.6% over fixed-rate WAG, 12.8% over water flooding, and 18.7% over gas flooding, while maintaining a competitive water cut of 0.734. The experimental results imply that the proposed WAG strategy can obtain higher NPVs than that of the benchmark algorithms.

With the rapid advancement of artificial intelligence, AI-generated content (AIGC) applications are becoming increasingly prevalent. However, it remains challenging to deliver high-quality AIGC services in edge scenarios that have real-time requirements and limited resources. In this paper, we propose BC-MFAIGC, a blockchain-enabled framework for mobile federated AIGC. The proposed BC-MFAIGC architecture coordinates edge computing, federated learning, and blockchain. Edge computing brings selected services closer to terminals, federated learning supports collaborative training without transferring raw user data, and blockchain provides transaction authentication, traceable update records, and governance functions. We evaluate the implemented denoising diffusion probabilistic model (DDPM) federated-training workflow under malicious updates using single-machine simulations. Under a medium-strength Gaussian weight-perturbation condition, the observed Fréchet Inception Distance (FID) increase is approximately 2.5% for BC-MFAIGC-Soft and 9.3% for NOBC-MFAIGC-Soft, compared with about 22% for FedAvg in the three-seed evaluation, while the worker-layer detection rate in the near-threshold regime is 9.03–14.29%, reflecting the difficulty of distinguishing malicious from benign updates when their score distributions overlap. Under the current attack condition, the mean FID across five seeds is 307.62 for BC-MFAIGC-Soft and 285.86 for NOBC-MFAIGC-Soft compared with 372.55 for FedAvg. The paired mean difference between BC-MFAIGC-Soft and FedAvg is $-64.93$ with an unadjusted 95% bootstrap CI ranging from $-96.64$ to $-29.53$. The corresponding difference between NOBC-MFAIGC-Soft and FedAvg is $-86.69$ with an unadjusted 95% bootstrap CI ranging from $-120.75$ to $-50.80$. Both comparisons have Holm-adjusted $p=0.25$. Separate protocol tests cover transaction authentication, block-integrity checking, and replay handling, and the simulated ledger configuration increases round wall-clock time by 8.6%.