Conventional drilling parameter optimization, heavily reliant on lagging lithology data from periodic mud logging, suffers from significant delays between formation change detection and parameter adjustment. This latency often leads to reduced Rate of Penetration (ROP), accelerated tool wear, and increased risk of drilling complications. To address this, this work introduces a closed-loop machine learning framework for real-time lithology identification and autonomous parameter optimization. Its core is a hybrid deep learning model (1D-CNN-LSTM) that establishes a direct mapping from surface drilling parameters, Weight on Bit (WOB), Rotary Speed (RPM), Torque, ROP, to formation lithology, deliberately excluding dependency on expensive Logging-While-Drilling (LWD) tools to ensure cost-effective and broad applicability. Upon lithology change detection, the system retrieves the historically optimal Mechanical Specific Energy (MSE) value for the identified rock type and solves an inverse MSE model to compute optimal WOB and RPM setpoints within operational constraints. Field validation in a comparative trial demonstrated the framework’s efficacy: the test well achieved a 17.4% increase in ROP, a 37.8% reduction in Non-Productive Time, and an 87.5% decrease in stuck pipe incidents compared to an offset well drilled conventionally.

Real-time and accurate inversion of drilling-fluid hydraulic parameters while drilling, together with the coordinated optimization of drilling parameters, is critical for safe and efficient drilling in deep and complex formations. Conventional methods are limited by single-source observations, insufficient prior constraints, weak surrogate-model generalization, and isolated parameter optimization. To address these issues, this paper proposes a three-layer intelligent decision-making framework. The first layer is a dual-prior-constrained temporal inversion module that fuses a pre-drill mechanistic baseline prior with an offset-well statistical prior and estimates plastic viscosity, yield point, annular cuttings concentration, and equivalent eccentricity from standpipe-pressure and rotary-torque observations through a four-term loss function. The second layer is a Fourier neural operator (FNO) surrogate trained on data generated by an in-house two-phase hydraulics solver. The third layer is a hydraulic–mechanical coupled multi-objective optimization framework that coordinates weight on bit, rotary speed, and flow rate using online Bayesian optimization and probabilistic safety constraints. Numerical experiments show that the dual-prior constraints reduce the inversion root-mean-square error by 42.7%, that the FNO surrogate is three orders of magnitude faster than numerical simulation while maintaining an accuracy above 98.3%, and that three-parameter optimization improves overall drilling efficiency by 28.5%. The framework provides an accurate, efficient, and robust solution for real-time intelligent drilling decision-making.

Real-time and accurate inversion of drilling-fluid hydraulic parameters while drilling, together with the coordinated optimization of drilling parameters, is critical for safe and efficient drilling in deep and complex formations. Conventional methods are limited by single-source observations, insufficient prior constraints, weak surrogate-model generalization, and isolated parameter optimization. To address these issues, this paper proposes a three-layer intelligent decision-making framework. The first layer is a dual-prior-constrained temporal inversion module that fuses a pre-drill mechanistic baseline prior with an offset-well statistical prior and estimates plastic viscosity, yield point, annular cuttings concentration, and equivalent eccentricity from standpipe-pressure and rotary-torque observations through a four-term loss function. The second layer is a Fourier neural operator (FNO) surrogate trained on data generated by an in-house two-phase hydraulics solver. All results reported here are obtained on such synthetic data: no field or laboratory measurements are used, and the offset-well statistical prior is prescribed—its mean from a regional depth trend and its covariance from an assumed inter-well variability—rather than fitted to measured offset-well logs. The third layer is a hydraulic–mechanical coupled multi-objective optimization framework that coordinates weight on bit, rotary speed, and flow rate using online Bayesian optimization and probabilistic safety constraints. Numerical experiments with 30 independent noise realizations show that the dual-prior constraints reduce the inversion root-mean-square error by up to 85% for the weakly identifiable parameters (cuttings concentration and equivalent eccentricity) and by 37% for plastic viscosity; that the FNO surrogate is about 240 times faster than the reference numerical simulation and attains a mean relative error of 0.31% for equivalent circulating density and 2.25% for annular pressure loss, about three times lower than the best baseline (a quadratic response surface, 0.95% and 7.16%), preserving that lead outside the training range, while remaining the only surrogate that can be evaluated on a different depth grid without retraining; and that three-parameter optimization improves the rate of penetration by 34.9%. At an equal total surrogate cost, the proposed online optimizer attains the highest mean gain of the methods compared (32.4% over 40 sliding windows, against 31.9% for an online NSGA-II with the same update frequency and 29.4% for a random-search control) while using 4.5 times fewer forward evaluations of the surrogate; the advantage over NSGA-II is small and not statistically resolved at this budget, whereas the advantage over random search is, and the proposed method also has the best worst-case window and the smallest window-to-window spread. The recommended operating points are re-verified with the reference forward solver. Imposing the two analytic closure relations of the forward model as soft physics residuals did not improve accuracy because the reference data satisfy them exactly and the constraint therefore carries no additional information. The framework provides an accurate, efficient, and robust solution for real-time intelligent drilling decision-making.

The WieZhou12 oilfield (also known as WZ12 oilfield, the same below) is in urgent need of development using large-scale volumetric fracturing technology since it is a typical complex fault-block oilfield with low porosity, low permeability, and no natural production capacity. To study the fracturing measures with surfactants in offshore oilfields like WZ12, the surfactant fracturing fluid types were experimentally selected based on their effect of decreasing interfacial tension and enhancing matrix wettability. The water cut law and oil displacement efficiency in displacement experiments were also analyzed, according to surfactant type and fluid characteristics. Next, using the numerical simulation software CMG, the study completed the integrated simulation of volumetric fracturing in the “injection–soaking–flowback” process. Finally, some critical parameters were optimized for the block model, including the quantity of injected fluids, the soaking time, and the rate of fluid flowback. The results showed that the most suitable surfactant was 0.5% ammonium lauryl polyether sulfate (ALES), which had a low interfacial tension of 1.7 × 10⁻² mN/m, a contact angle of 20.071° with the core, and a 52% oil displacement efficiency. From the simulations, the suggested production parameters for energy storage fracturing are as follows: a daily injection volume of 600 m³/d, a soaking time of 25 days post fracturing, and a fluid production rate of 270 m³/d. The findings of this study establish a significant theoretical foundation for optimizing surfactant type and provide construction advice for the integrated measure of fracturing, well shut-in, and production in offshore oilfields.