智能钻完井技术研究综述
Intelligent Drilling and Completion: A Review
石油与天然气工程智能化已成为行业发展的必然趋势，其中智能钻完井技术可以大幅提高钻井效率和钻遇率，降低施工成本，被视为油气领域的一项变革性技术和前沿热点。机理-数据融合的智能建模、数字孪生等人工智能方法及其在油气钻完井工程领域的应用已取得广泛关注和关键进展，但是智能钻完井技术研究仍然处于初级阶段。在人工智能、大数据等前沿技术与油气钻完井工程深度融合的过程中，智能钻完井场景体系、多源多尺度数据治理、机理-数据混合驱动、模型可解释性、模型迁移性和不确定性建模等面临诸多挑战。为此，本文系统提出了钻完井人工智能应用场景体系，全面阐述了各场景下的智能技术及研究进展，深入探讨了智能钻完井技术未来发展的重点方向，为人工智能技术落地油气钻完井工程提供参考。
The application of artificial intelligence (AI) has become inevitable in the petroleum industry. In drilling and completion engineering, AI is regarded as a transformative technology that can lower costs and significantly improve drilling efficiency (DE). In recent years, numerous studies have focused on intelligent algorithms and their application. Advanced technologies, such as digital twins and physics-guided neural networks, are expected to play roles in drilling and completion engineering. However, many challenges remain to be addressed, such as the automatic processing of multi-source and multi-scale data. Additionally, in intelligent drilling and completion, methods for the fusion of data-driven and physicsbased models, few-sample learning, uncertainty modeling, and the interpretability and transferability of intelligent algorithms are research frontiers. Based on intelligent application scenarios, this study comprehensively reviews the research status of intelligent drilling and completion and discusses key research areas in the future. This study aims to enhance the berthing of AI techniques in drilling and completion engineering.
| Application | Author | Algorithm | Inputs | Content and innovation |
|---|---|---|---|---|
| Prediction of drillability | Gamal et al. [6] and | ANN | Includes WOB, RPM and GA. | Combination of mechanism model and ANN algorithm |
| Li and Cheng [8] | GA and ANN | Bit type, drilling time, rotation, WOB, etc. | IGA-ANN avoids the local convergence in classical GA | |
| Prediction of bit wear | Asadi [9] | ANN | UCS, BTS, and rock brittleness | Combination of mechanism model and AI algorithm |
| Sirdesai et al. [10] | MVRA, ANN, and ANFIS | Includes compressive and tensile strength and porosity | Comparison of various algorithms | |
| Kahraman et al. [11] | Regression analysis | Includes UCS and BTS | Predict the value of CAI | |
| Lakhanpal and Samuel [12] | Adaptive data analytics | Drilling parameters and ROP | Using EMD | |
| Prediction of lithology | Zhekenov et al. [13] | RF | RPM, ROP, WOB, TOB and SPP | Integrating ML with the mechanism |
| Authors | Methods/algorithms | Inputs | Content/innovation |
|---|---|---|---|
| Batruny et al. [14] | ANN and Monte-Carlo | WOB, RPM, hydraulic, and formation properties | ML-assisted bit selection and optimization |
| Abbas et al. [15] | ANN and GA | Nineteen parameters (i.e., geology, bit) | Drill bit selection and optimization |
| Tortrakul et al. [16] | Big data analysis | Database of neighboring wells | Bit and BHA selection |
| Okoro et al. [17] | ANN, PCA, and PSO | Drill bit images and drilling parameters | Drill bit selection |
| Rashidi et al. [18] | Clustering algorithm | Drilling parameters | Drill bit design |
| Physics-based models | Real-time drilling parameters | Bit wear evaluation | |
| Gidh et al. [19] | ANN | Drilling parameters of neighboring wells | Bit wear prediction and management |
| Losoya et al. [20] | KNN, RF, and ANN | Includes WOB, RPM, TOB, ECD and MSE | Drilling condition recognition |
| Authors | Methods/algorithms | Inputs | Content/innovation |
|---|---|---|---|
| Liao et al. [21] | ANN | Thrust, RPM, flushing media, and compressive strength | Bee colony optimize ANN |
| Mehrad et al. [22] | COA, PSO, GA, SVR, MLP, and LMR | UCS, FR, WOB, depth, MD, and RPM | Use a variety of algorithm |
| Gan et al. [23] | Hybrid SVM and eight other methods | Depth, WOB, RPM, and flow rate | A hybrid model |
| Anemangely et al. [24] | MLP-COA and MLP-PSO | Rotary speed, WOB, and flow rate | MLP is combined with COA and PSO |
| Abbas et al. [25] | ANN | MD and other 19 parameters | Features are optimized using FSCARET |
| Hegde et al. [26] | Integrated RF, ANN and linear regression. | WOB, RPM, and flow rate | A better integration model |
| Han et al. [27] | ANN and LSTM | Includes well logging and mud logging data | Timing relation of ROP |
| Sabah et al. [28] | FT, RF, SVM, MLP, BF, and MLP‒PSO | Includes WOB, RPM and flow rate | Comparison of multiple prediction models |
| Soares et al. [29] | RF, SVM, ANN | Depth, WOB, RPM, and flow rate | The random forest has higher accuracy |
| Diaz et al. [30] | MR and ANN | Includes WOB and normal compaction | Fast Fourier transform improves the model |
| Authors | Methods/algorithms | Inputs | Content/innovation |
|---|---|---|---|
| Hegde and Gray [31] | RF and PSO | Includes WOB, RPM, Flow-rate and rock strength | Coupling ROP, MSE and TOB models |
| Arabjamaloei and Shadizadeh [32] | ANN and GA | Includes bit type, RPM, WOB, bit tooth wear and ECD | GA optimized ANN to obtain the optimal parameters |
| Bataee and Mohseni [33] | ANN, LM, and GA | Includes bit diameter, depth, WOB, RPM and MW | Using GA to optimize real-time drilling parameters |
| Gan et al. [34] | Nadaboost-ELM and RBFNN‒IPSO | Includes FD, depth, SWOB, RPM and MW | A novel two-level intelligent modeling method |
| Oyedere and Gray [35] | LR, LDA, QDA, SVM and RF | Includes WOB, flow rate, RPM and UCS, | The best classifier for each formation |
| Hegde et al. [36] | RF and gradient ascent | Includes WOB, RPM and UCS | Consider the effect of drilling vibrations |
| Momeni et al. [37] | ANN and GA | Includes hole size, WOB, RPM and MW | Using ROP model to optimize bit |
| Jiang and Samuel [38] | BRNN and ACO | Includes depth, WOB, RPM, mud FR and GR | ACO and BRNN were combined to optimize ROP |
| Zhang et al. [39] | K-means | Includes depth, AC, GR, density, and UCS | Enhancing ROP with lithology |
| Moazzeni and Khamehchi [40] | ROA | Includes WOB and MSE | Use ROA algorithm to optimize ROP |
| Authors | Algorithms | Objectives | Contents |
|---|---|---|---|
| Wang et al. [41] | Computer vision | Images showing oil and gas distribution | Consider the reservoir-encountered rate as the target and the build-up rate as the constraint |
| Selveindran et al. [42] | LSTM | Well depth, inclination angle, and azimuth angle | RNN classifies wells with similar trajectories |
| Lee et al. [43] | Genetic algorithm | Production rate and cost | Improving both profit and cumulative production |
| Vlemmix et al. [44] | Gradient-based search method | Net present value | Significant improvement in NPV of the well |
| Zheng et al. [45] | MOC‒PSO | Length, torque, and well strain energy | Constructed neighbors affected the search |
| Mansouri et al. [46] | MOGA | Length and torque | The adaptive function for parameter setting |
| Wang et al. [47] | Heuristic algorithm | Total trajectory length, well profile energy, and target hitting | Optimal clusters sidetracking horizontal |
| Zheng et al. [48] | Analytical target cascading | Length, torque, and profile energy | Decomposition of the objective functions yields a better result |
| Liu and Samuel [49] | Minimum energy method | Minimum well profile energy criterion | Less electric power consumption |
| Li and Tang [50] | Mogi-coulomb condition with MCM | Measured depth | The stability of wellbore trajectory improved |
| Khosravanian et al. [51] | GA, ABC, ACO, and HS | Measured depth | ACO took less computational time than GA |
| Authors | Algorithms | Inputs/objectives | Contents |
|---|---|---|---|
| Vabø et al. [52] | Tree search algorithm | Well location and target location | Evaluating results for the optimization of drilling based on risk, value, and cost |
| Koryabkin et al. [53] | Lasso regression and RF | Includes block position, WOB, ROP and SPP | The result shows MedAE of depth, inclination, and azimuth |
| Tunkiel et al. [54] | RNN and MLP | Logging parameters and well inclination parameters | The study can predict 23 m, while the existing methods can only predict 7 m |
| Noshi and Schubert [55] | ANN, AdaBoost, RF, and GBM | Includes BHA, parameters of drill bit and logging parameters | The side forces in the form of seven dominant factors are primarily responsible |
| Li et al. [56] | PSO with AHP | Target hitting, lowest cost, and least drilling string friction | Numerical solutions are computed |
| Atashnezhad and Wood [57] | PSO | True measured depth | Meta optimization helped PSO to perform better |
| Sha and Pan [58] | FSQGA | True measured depth | The Fibonacci series enhanced the convergence speed |
| Xu and Chen [59] | Bat algorithm optimizer | True measured depth | Stable wellbore trajectory designed |
| Halafawi and Avram [60] | MCM | Includes wellbore stability and stress determination | Optimal horizontal wellbore trajectories are designed |
| Authors | Methods/algorithms | Inputs/objectives | Contents |
|---|---|---|---|
| Zalluhoglu et al. [61] | Physics-based and self-learning model | Real-time parameters from RSS, MWD, and LWD | Steering decisions given the BHA configuration |
| Sugiura et al. [62] | Physics-based models | Real-time parameters from RSS, MWD, and LWD | Saving four days compared with non-high-dogleg RSS runs |
| Zhang et al. [63] | Dual-loop feedback cooperative control method | Real-time parameters from RSS, MWD, and LWD | Trajectory tracking control for RSSs |
| Song et al. [64] | Physics-based models | Real-time parameters from RSS | Tracking-based tool faces positioning on RSS |
| Kullawan et al. [65] | Discretized stochastic | Real-time parameters from LWD | Decision-oriented geosteering |
| Application | Authors | Algorithms | Input parameters | Content/innovation |
|---|---|---|---|---|
| Prediction of formation pressure pre-drilling | Kazei et al. [66] | CNN and LSTM | Zero-offset VSP and well-logging | Predict the rock mechanics of the lower part of the bit |
| Monitoring of formation pore pressure in real-time | Rashidi and Asadi [67] | ANN | MSE and DE | Using MSE and DE to predict the formation pressure |
| Ahmed et al. [68] | ANN | Pump rate, SPP, RPM, ROP, torque, and WOB | Using mechanical and hydraulic parameters to monitor formation pressure | |
| Vefring et al. [69] | LM and Kalman filter | Pump pressure, BHP, outlet rates | Inversion of the pore pressure based on the drilling parameters | |
| Post-drilling assessment of formation pore pressure | Zambranoet al [70] | DT, RF, SVM, and AdaBoost | Includes gamma-ray, bulk density and deep resistivity | Using the parameters of the normal compaction trend line as the input |
| Mylnikov et al. [71] | ANN | TVD and acoustic well-logging | Using the vertical depth and sonic logging to establish a formation pressure evaluation model | |
| Booncharoen et al. [72] | Quantile, Ridge, and XGBoost | Includes net sand thickness, porosity and water saturation | Considering the influence of reservoir parameters | |
| Naeini et al. [73] | DNN | Includes compressional velocity, gamma-ray and density | Three neural network models are connected in series to predict geomechanical parameters |
| Application | Authors | Algorithms | Input parameters | Main content |
|---|---|---|---|---|
| Bottom hole pressure | Liang et al. [74] | GA‒BPNN | Includes inlet and outlet flow, overflow time and depth | Real-time prediction of BHP |
| Al Shehri et al. [75] | FCNN and LSTM | Water-gas ratio, well depth, wellhead temperature, and pressure | Considering the sequence of BHP and the flow mechanism | |
| Fruhwirth et al. [76] | BPNN and SVM | Includes engineering parameters and combine parameters | Integration parameters enhance model generalization ability | |
| Zhang and Tan [77] | Naive Bayesian | Engineering parameters and combination parameters | Improved the prediction accuracy | |
| Li et al. [78] | Mechanism-based BPNN models | Incline angle, surface velocity, and surface tension | Broadened the model application range | |
| Gola et al. [79] | Grey box | Includes pump flow, throttle valve opening, back pressure and pump flow rate | Combine mechanism and AI model for a stable result | |
| Feili et al. [80] | Neural fuzzy system | Various engineering parameters | Higher prediction accuracy | |
| Ashena et al. [81] | ANN | Various engineering parameters | Higher prediction accuracy | |
| ECD | Alsaihati et al. [82] and Alkinani et al. [83] | ANN | Various engineering parameters | Various AI models were compared |
| Han et al. [84] | ARIMA‒BP | BHP sequence | ARIMA‒BP model captures the linear and nonlinear trend | |
| Elzenary et al. [85] | Adaptive fuzzy neural network | ROP, inlet density, and riser pressure | Fuzzy logic enhances generalization |
| Application | Author | Algorithms | Input parameters | Main content |
|---|---|---|---|---|
| Wellbore stability | Jahanbakhshi et al. [86] | PCA and ANN | Geological, engineering parameters, and mud properties | PCA implements dimension reduction of input factor |
| Okpo et al. [87] | ANN | ROP, pressure, MD, and other 26 parameters | Integrated drilling, geological and reservoir information | |
| Lin et al. [88] | BRNN and SVM | ROP, BHA, depth, and other 20 parameters | Noise and variation in data were eliminated by EMD | |
| Tewari [89] | RF, ANN, and SVM | Includes FR, well angle, well depth and ROP | Accurately predict wellbore stability in deviated wells | |
| Drilling risk | Mohan et al. [90] | Monte Carlo | Includes well trajectories, completions and historical events | Risk can be integrated into the system in real-time to ensure model timeliness |
| Li et al. [91] | FL | Drilling monitoring parameters | Grade classification of nine risks | |
| Yin et al. [92] | Bayes and FL | Formation pressure, fluid density, and drilling parameters | The probability profile of risk is established by FL | |
| Blowout and gas kick | Sule et al. [93] | Bayesian networks | Wellhead back pressure, BHP, etc. | A 7-level classification of blowout risk |
| Yin et al. [94] | LSTM and RNN | Includes flow difference, pool volume and WOB | A 5-level classification of gas kick | |
| Yin et al. [95] | LSTM | Includes flow difference, pool volume and WOB | Data preprocessing reduces late warning time | |
| Muojeke et al. [96] | ANN | Includes downhole pressure, inlet‒outlet flow and density | Data from laboratory risk experiments | |
| Lost circulation | Liang et al. [97] | ANN and PSO‒SVR | Includes pore pressure, fracture pressure and BHP | A risk level index was constructed by FL |
| Pang et al. [98] | Mixture density networks | FR, density, cell volume, and hook load | Accurate warning of loss risk | |
| Li et al. [99] | BPNN, SVM and RF | Includes MD, filtration loss and pump pressure | Real-time prediction of loss level | |
| Hou et al. [100] | ANN | Formation, fluid, and engineering parameters | Well loss probability distribution of six grades | |
| Alkinani et al. [101] | SVM | MW, equivalent loss density, and yield point | Classification and identification of loss degree | |
| Shi et al. [102] | RF and SVM | Includes flow, pressure and temperature | Data preprocessing can reduce detection time | |
| Stuck | Mopuri et al. [103] | CNN, SVN, and RF | Includes Torque, ROP and bit position | Reverse learning of a few sample data |
| Al Dushaishi et al. [104] | DT | Includes rotation speed, BHA and fluid parameters | Sticking prediction under different conditions |
| Application | Authors | Algorithms | Input parameters | Main content |
|---|---|---|---|---|
| Wellbore pressure | Siahaan et al. [105] | Adaptive PID | Wellhead throttle valve | Based on real-time data, not limited by prior knowledge |
| Zhou et al. [106] | Adaptive predictor control | Backpressure pump and throttle valve | Considered time delay of wellbore pressure transmission | |
| ECD | Yin et al. [107] | Wellhead control equipment | Backpressure pump and throttle valve | Automatic management of gas kick |
| BHP | Pedersen and Godhavn [108] | MPC | Backpressure pump and throttle valve | Pressure control under different conditions |
| Li et al. [109] | Adaptive controller | Backpressure pump and throttle valve | Robust to BHP noise | |
| Nandan and Imtiaz [110] | NMPC | Backpressure pump, throttle valve, FR | Constant BHP after kick | |
| Nandan et al. [111] | Robust gain switching control | Backpressure pump | The robustness of the controller is enhanced | |
| Sule et al. [112] | NMPC | Choke manifold | Automatic management of gas kick |
| Authors | Algorithm | Inputs | Content and innovation |
|---|---|---|---|
| Tran et al. [118] | KNN | Surface drilling data | Identified brittle and frackable zones |
| Palmer [119] | Fuzzy C-means | Acoustic logging and natural fracture logging | Classified similar shale formations |
| Xu et al. [120] | GA and adaptive evolution | Reservoir structure grid and hydraulic parameters | The azimuth and perforation clusters were optimized |
| Dalamarinis et al. [121] | RR and RF | Fracturing process parameters | Reduce inter-well interference and improve fracture complexity |
| Rahmanifard and Plaksina [122] | Genetic, differential evolution and PSO | Includes well spacing, porosity and permeability | PSO has the highest net present value |
| Gong et al. [123] | Clustering algorithm and ANN | Rock structure and geomechanical characteristics | ANN is used to identify brittle clusters |
| Application | Authors | Algorithm | Inputs | Content and Innovation |
|---|---|---|---|---|
| Event recognition | Ramirez and Iriarte [124] | SVM and logistic regression | Includes pump pressure, injection rate and proppant concentration | Automatically mark the beginning and end of hydraulic fracturing |
| Decision tree | The pressure changes are analyzed and abnormal conditions are identified. | |||
| Shen et al. [125] | CNN, U-net | Mark fracturing start and end points | ||
| Pump pressure prediction | Ben et al. [126] | MLP, CNN and RNN | Real-time prediction of wellhead pressure | |
| Casing failure recognition | Li et al. [78] | Random forest | Casing failures are identified | |
| Screen-out prediction | Maučec et al. [127] | CART | The prediction of screen-out, and identifying the affecting factors | |
| Sun et al. [128] | CNN‒LSTM | Includes pump pressure and injection rate | Combination of physics-based inverse slope method and newly-developed machine learning techniques | |
| Yu et al. [129] | GHMMs | Includes pump pressure, injection rate and proppant concentration | Successful warning about 8.5 min before screen-out | |
| Hu et al. [130] | ARMA | The early warning rules were designed based on the prediction of pump pressure |
| Application | Authors | Algorithm | Inputs | Content and Innovation |
|---|---|---|---|---|
| Productivity prediction | Pankaj et al. [131] | GradBoost | Includes fluid type; proppant quantity; pumping rate and BHP | Provide the best directional response in real-time |
| Bhattacharya et al. [132] | RF | Includes fracturing length and casing pressure, tubing pressure | Optical fiber parameters are introduced to improve the accuracy of the model | |
| Al Shehri et al. [75] | Boost | Includes the number of stages, propping dose and injected fluid volume | Model integration and uncertainty quantification | |
| Liu et al. [133] | ANN | Includes length of fracturing, fracturing clusters and formation thickness | The underlying algorithm of time series analysis | |
| Fracturing parameter optimization | Duplyakov et al. [134] | CatBoost | Injected fluid volume, TVD, perforation angle, perforation spacing | The recommendation system for optimizing fracturing parameters |
| Duplyakov et al. [134] | CatBoost | Includes formation thickness, angle, and formation pressure | Euclidean distance was used to find similar wells |
| Application | Authors | Algorithms | Input parameters | Main content |
|---|---|---|---|---|
| Completion design optimization | Ma et al. [135] | Augmented AI | Engineering and geological properties | Model sensitivity analysis |
| Production prediction | Klie [136] | RBF | Production data and time | The fusion of physics-based models and data-driven models |
| Inflow performance in wellbore | Tariq et al. [137] | SVM‒PSO | Production data and time | The data source is a numerical simulation |
| Dynamic production optimization | Prosvirnov et al. [138] | — | Wellbore inflow and pressure distribution | Based on an intelligent completion system |
| Wellbore production profile | Chaplygin et al. [139] | Random forest | The number of tracers | Determine the inflow distribution based on the number of tracers |
| Multilateral inflow prediction | Khamehchi et al. [140] | ANN | ICV and production parameters | Prediction of downhole flow conditions |
| Multilateral inflow optimization | Aljubran and Horne [141] | ANN | ICV and production parameters | Optimization of downhole flow |
| Well and reservoir management | Bello et al. [142] | Data-driven | Downhole monitoring data | Real-time reservoir management |
| Completion design | Solovyev and Mikhaylov [143] | Data-driven | Production log data | Layout of the AICD |
| ICD and packer optimization | Goh et al. [144] | Data-driven | ICD and packer layout | Dynamic optimization of a single well |
| Authors/institute | Scope | Involved systems | Content/innovation |
|---|---|---|---|
| Shishavan et al. [145] | MPD | Rock-breaking and hydraulic system | Combining ROP and BHP into a comprehensive controller for MPD |
| Ambrus et al. [146] | Model building | Rock-breaking and drill-string system | Modeling bit-rock interaction and drill-string dynamics |
| Zhou et al. [147] | Drilling optimization | Rock-breaking and hydraulic system | Multi-objective optimization and decision-making combing ROP and MPV |
| NORCE [148‒150] | Autonomous drilling | Includes rock-breaking, drill-string and hydraulic system | Autonomous decision-making system while drilling |
| Texas A&M University [151‒152] | Drilling simulator | Includes rock-breaking, drill-string and hydraulic system | Drilling simulator development |
| University of Stavanger [153‒155] | autonomous drilling rig | — | Designing a small-scale autonomous drilling rig and control system |
| Digital twin | — | Architectures of drilling optimization, decision-making, and control based on digital twin | |
| eDrilling [159] | Business software | Includes rock-breaking, drill-string and hydraulic system | Real-time modeling, monitoring, optimization, and visualization of the drilling process |
| DrillOps [160] | Business software | — | Real-time drilling risk monitoring, optimization, and decision-making of the drilling process |
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