Search Results (7)

Borehole trajectory planning under complex geological conditions poses significant challenges for intelligent drilling systems. To tackle this issue, a novel optimization framework is developed, leveraging the Soft Actor-Critic (SAC) algorithm enhanced by a self-attention mechanism. A three-dimensional heterogeneous geological model is constructed via generative adversarial networks (GANs), incorporating key formation features such as lithology, pressure, and fault zones. A tailored multi-objective reward function is introduced, balancing directional convergence, trajectory smoothness, obstacle avoidance, and formation adaptability. The self-attention mechanism is embedded into both the actor and critic networks to strengthen the agent’s capacity for spatial perception and decision stability. The proposed approach enables the agent to adaptively generate control sequences for efficient trajectory planning in highly variable formations. Experimental results demonstrate that the model exhibits superior convergence stability, improved curvature control, and enhanced obstacle avoidance, highlighting its potential for intelligent trajectory planning in challenging drilling environments. Full article

As an important target for deep marine shale gas exploration, shale reservoirs near denudation areas have enormous resource potential. Based on the impression method, the sedimentary paleogeomorphology near the denudation area is identified as three units: the first terrace, the second terrace, and the third terrace. At the second terrace where Well Z212 is located, the thickness of the Longmaxi Formation first section is only 0.8 m, and the continuous thickness of the target interval is only 4.3 m, making it a typical thin shale reservoir. By integrating petrology, mineralogy and the seismic method, the thin shale reservoir is characterized. Compared to shale reservoirs far away from the denudation area, the Well Z212 (near denudation area) production interval (Wufeng Formation first section) has high porosity (6%–10%), moderate TOC (3%–4%), a high carbonate mineral content (10%–35%), and a high gas content (>7 m³/t). The correlation between the total porosity of shale and the density of high-frequency laminations is the strongest, indicating that the silt laminations have a positive effect on pore preservation. There is a significant positive correlation between carbonate content and the volume of mesopores and macropores, as well as the porosity of inorganic pores. It is suggested that carbonate minerals are the main carrier of inorganic pores in Well Z212, and the pores are mainly composed of mesopores and macropores. Under the condition of being far away from the fault zone, even near the denudation area, it has good shale gas preservation characteristics. The key development technologies consist of integrated geo-steering technology, acidification, and volume fracking technology. Based on geological characteristics, the fracturing process optimization of Well Z212 has achieved shale reservoir stimulation. Full article

Facing engineering challenges of real-time and high-precision recognition of strongly heterogeneous formations during directional drilling, it is crucial to address the issues of sparse lithology geological labels and multi-source lithology identification from LWD data. This paper proposes a real-time intelligent recognition method for strongly heterogeneous formations based on multi-parameter logging while drilling and drilling engineering, which can effectively guide directional drilling operations. Traditional supervised learning methods rely heavily on extensive lithology labels and rich wireline logging data. However, in LWD applications, challenges such as limited sample sizes and stringent real-time requirements make it difficult to achieve the accuracy needed for effective geosteering in strongly heterogeneous reservoirs, thereby impacting the reservoir penetration rate. In this study, we comprehensively utilize LWD parameters (six types, including natural gamma and electrical resistivity, etc.) and drilling engineering parameters (four types, including drilling rate and weight on bit, etc.) from offset wells. The UMAP algorithm is employed for nonlinear dimensionality reduction, which not only integrates the dynamic response characteristics of drilling parameters but also preserves the sensitivity of logging data to lithological variations. The K-means clustering algorithm is employed to extract the deep geo-engineering characteristics from multi-source LWD data, thereby constructing a lithology label library and categorizing the training and testing datasets. The optimized CatBoost machine learning model is subsequently utilized for lithology classification, enabling real-time and high-precision geological evaluation during directional drilling. In the Hugin Formation of the Volve field in the Norwegian North Sea, the application of UMAP demonstrates superior data separability compared with PCA and t-SNE, effectively distinguishing thin reservoirs with strong heterogeneity. The CatBoost model achieves a balanced accuracy of 92.7% and an F1-score of 89.3% in six lithology classifications. This approach delivers high-precision geo-engineering decision support for the real-time control of horizontal well trajectories, which holds significant implications for the precision drilling of strongly heterogeneous reservoirs. Full article

Existing methods for estimating formation boundaries from well-log data only analyze the formation along the wellbore, failing to capture changes in the 3D formation structure around it. This paper presents a method for real-time 3D formation boundary interpretation using readily available well logs and seismic image data. In the proposed workflow, the mean formation boundary is estimated as a curve following the well path. 3D surfaces are then fitted through this boundary curve, aligning with the slopes and features in the seismic image data. The proposed method is tested on both synthetic and field datasets and illustrates the capabilities of accurate boundary estimation near the well path and precise representation of boundary shape changes further away from the well trajectory. With this fully automated geological interpretation workflow, human bias and interpretation uncertainty can be minimized. Subsurface conditions can be continually updated while drilling to optimize drilling decisions and further automate the geosteering process. Full article

Fast and precise geo-steering and geo-navigation upon well drilling are the key parameters for improved well targeting, optimal well placement, and maximal hydrocarbon recovery. To advance geo-steering parameters, we propose a new approach to on-site formation evaluation through the use of fluorescent tracers for drill cuttings tagging according to the depth of origin. Cuttings labelling at the drill bit site is followed by near-real-time drilling depth correlation at the well-head via a camera and AI image recognition systems. To suite the drilling process, the engineered tracers should match to the rheology of the utilized drilling mud. This study was performed to comprehensively investigate the effect of fluorescent tracers on the rheological properties of oil-based drilling mud (OBM) and to determine the optimal quantities of the tracers’ addition. We evaluated critical mud characteristics including electrical stability, thixotropic parameters, shear stress, gel strength, plastic viscosity, and yield point as prepared and in the presence of fluorescent tracers at the range of 1 to 20 wt.%. Additionally, the mud’s effects on the long-term stability of the fluorescent tracers were assessed via hot-rolling tests in conditions mimicking downhole conditions, with the aim of determining the tags’ feasibility for drill cuttings labelling applications. The study also examines the recovery potential of the tracers and their reusability in the drilling process. This investigation provides valuable insights into the potential application of fluorescent tracers for downhole drill cuttings depth correlation which will improve geo-steering works. Full article

The accurate prediction of underground formation lithology class and tops is a critical challenge in the oil industry. This paper presents a machine-learning (ML) approach to predict lithology from drilling data, offering real-time litho-facies identification. The ML model, applied via the web app “GeoVision”, achieves remarkable performance during its training phase with a mean accuracy of 95% and a precision of 98%. The model successfully predicts claystone, marl, and sandstone classes with high precision scores. Testing on new data yields an overall accuracy of 95%, providing valuable insights and setting a benchmark for future efforts. To address the limitations of current methodologies, such as time lags and lack of real-time data, we utilize drilling data as a unique endeavor to predict lithology. Our approach integrates nine drilling parameters, going beyond the narrow focus on the rate of penetration (ROP) often seen in previous research. The model was trained and evaluated using the open Volve field dataset, and careful data preprocessing was performed to reduce features, balance the sample distribution, and ensure an unbiased dataset. The innovative methodology demonstrates exceptional performance and offers substantial advantages for real-time geosteering. The accessibility of our models is enhanced through the user-friendly web app “GeoVision”, enabling effective utilization by drilling engineers and marking a significant advancement in the field. Full article

Data telemetry is a critical element of successful unconventional well drilling operations, involving the transmission of information about the well-surrounding geology to the surface in real-time to serve as the basis for geosteering and well planning. However, the data extraction and code recovery (demodulation) process can be a complicated system due to the non-linear and time-varying characteristics of high amplitude surface noise. In this work, a novel model fuzzy wavelet neural network (FWNN) that combines the advantages of the sigmoidal logistic function, fuzzy logic, a neural network, and wavelet transform was established for the prediction of the transmitted signal code from borehole to surface with effluent quality. Moreover, the complete workflow involved the pre-processing of the dataset via an adaptive processing technique before training the network and a logistic response algorithm for acquiring the optimal parameters for the prediction of signal codes. A data reduction and subtractive scheme are employed as a pre-processing technique to better characterize the signals as eight attributes and, ultimately, reduce the computation cost. Furthermore, the frequency-time characteristics of the predicted signal are controlled by selecting an appropriate number of wavelet bases “N” and the pre-selected range for $p_{ij}^3$ to be used prior to the training of the FWNN system. The results, leading to the prediction of the BPSK characteristics, indicate that the pre-selection of the N value and $p_{ij}^3$ range provides a significantly accurate prediction. We validate its prediction on both synthetic and pseudo-synthetic datasets. The results indicated that the fuzzy wavelet neural network with logistic response had a high operation speed and good quality prediction, and the correspondingly trained model was more advantageous than the traditional backward propagation network in prediction accuracy. The proposed model can be used for analyzing signals with a signal-to-noise ratio lower than 1 dB effectively, which plays an important role in the electromagnetic telemetry system. Full article