Fault Interpretation Research Articles

Coal seam gas reservoir characterisation based on high-resolution image logs from vertical and horizontal wells: A case study

Drilling horizontal surface-to-inseam (SIS) wells along low permeability coal seams, e.g. <10 mD, is a crucial technology to increase the drainage area for this type of unconventional reservoir. For the first time in the Bowen Basin, a continuous 1900 m long formation resistivity-based high-resolution image log from a SIS well is used to investigate key coal reservoir characteristics including faults, fractures, coal plies, porosity and permeability. This study presents a workflow from image log interpretation to discrete fracture network (DFN) modelling and then to well performance analysis. The interpreted faults from the image logs were used to analyse the uncertainty of fault interpretation from 3D seismic data. The image log was also used in analysing the variation of fracture distribution vertically and horizontally and compared to similar data obtained from vertical wells. All these data were used to understand the variation in productions from three nearby chevron well groups. Results show that the fracture density decreases with increasing Gamma-ray response from the in-seam section, including the intra-seam partings. Regionally, the orientation of fractures and cleats is parallel, sub-parallel or even perpendicular to the regional maximum stress orientation; however, in the study area, the majority of fractures are generally parallel or near parallel to the maximum stress orientation. This study confirmed the trend that the orientation of fractures/cleats rotated from NE to NW from the study area to the north. Because the maximum stress orientation is NNE, this cleat orientation will yield a decrease in coal permeability from south to north. This can explain at least partially the decrease in well performance from south to north. Based on the modelled fractures and their apertures, the upscaled fracture porosity ranged from 0% to 0.01%, with fracture permeability in the x-direction (easting direction) ranging mainly from 1 mD to 10 mD, and with the ratio of permeability in the x-direction to y-direction (northing direction) ranging from 5 to 20.

High-resolution seismic faults interpretation based on adversarial neural networks with a regularization technique

Geologic fault detection at high precision and resolution is the key for fine structure and reservoir modeling. Previous studies using neural networks for fault segmentation mainly focus on the local features of the targets and train the networks using synthetic data sets. To increase the fault segmentation resolution only using a limited amount of seismic field data, we develop an adversarial neural network architecture for high-resolution identification of faults (FaultAdvNet) taking advantage of global feature fusion. The architecture consists of (1) a light-weight segmentation module (approximately 0.49 M parameters), (2) a feature fusion module considering reflectors of faults and surrounding stratums, and (3) a discriminator module acting as a regularization term. Case studies using seismic field data from the Gulf of Mexico show an overwhelming performance improvement of the FaultAdvNet when compared with other fault detection methods. The FaultAdvNet picks all of the faults with sufficiently high confidence and low prediction risk. The predicted faults of the FaultAdvNet have good continuity and show clear boundary with fault probability values mainly ranging from 0.95 to 1. Saliency analysis also suggests that the FaultAdvNet can focus on the target at a sufficiently higher resolution (dozens of meters). Functionality experiments verify the mechanisms of the feature fusion module and the discriminator module in FaultAdvNet. We consider that a neural network (such as the discriminator) can serve as a data-driven regularization term to constrain the target network (the segmentation network) efficiently, especially given a limited amount of seismic data.

Influence of multi-trend major fault reactivation during multiphase rifting: Beier Depression, Hailar Basin, NE China

AbstractComplex fault patterns associated with rift development in the Hailar Basin were largely influenced by the Mongolia–Okhotsk Ocean and Palaeo-Pacific tectonic regimes during the Late Jurassic to Early Cretaceous periods. Based on 3D seismic data from the Beier Depression in the Hailar Basin, we characterized the reactivation history of multi-trend major faults and examined the evolution of the Beier Depression during the Early Cretaceous period. NE–SW-, NW–SE- and ENE–WSW-oriented major faults originated from strike-slip-associated structures that were pre-existing fabrics and then were reactivated and propagated upward under extensional regimes in the Late Jurassic. During the syn-rift stage (K1t–K1n), the Hailar Basin was in a NNW–SSE- to NW–SE-oriented extensional setting, and major faults of all orientations were active. There was tectonic quiescence (K1n1L) between the syn-rift stages (a rifting transition stage). The short compression stage after the syn-rift stage caused regional compressional deformation. During the post-rift stage (K1d–K1y), the extension direction rotated to an E–W orientation, and a new population of N–S-trending faults formed together with the reactivation of NE–SW- and ENE–WSW-trending major faults. Structural analysis shows that the major ENE–WSW-trending major faults were polycyclic growth faults reactivated via an upward propagation mode and that the NE–SW-trending faults were dip linkage faults reactivated via a dip linkage mode. The reactivation intensity of the NE–SW-trending major faults was stronger than that of the ENE–WSW-trending major faults. These results demonstrate the differences in the evolution of the different trending faults in the same tectonic regime, and the complexity of the final fault patterns in the Beier Depression was produced by differences in the reactivation of major faults. The originate interpretation of the multi-trend major faults in the Hailar Basin provides new insights into fault generation, and the classification of fault growth also has useful implications for future research on multiphase rifts.

Mapping Cretaceous faults using a convolutional neural network – A field example from the Danish North Sea

The mapping of faults provides essential information on many aspects of seismic exploration, characterisation of reservoirs for compartmentalisation and cap-rock integrity. However, manual interpretation of faults from seismic data is time-consuming and challenging due to limited resolution and seismic noise. In this study, we apply a convolutional neural network trained on synthetic seismic data with planar fault shapes to improve fault mapping in the Lower and Upper Cretaceous sections of the Valdemar Field in the Danish North Sea. Our objective is to evaluate the performance of the neural network model on post-stack seismic data from the Valdemar Field. Comparison with variance and ant-tracking attributes and a manual fault interpretation shows that the neural network predicts faults with more details that may improve the overall geological and tectonic understanding of the study area and add information on potential compartmentalisation that was previously overlooked. However, the neural network is sensitive to seismic noise, which can distort the fault predictions. Therefore, the proposed model should be treated as an additional fault interpretation tool. Nonetheless, the method represents a state-of-the-art fault mapping tool that can be useful for hydrocarbon exploration and CO2 storage site evaluations.

The Interpretation of Seismogenic Fault of the Maduo Mw 7.3 Earthquake, Qinghai Based on Remote Sensing Images—A Branch of the East Kunlun Fault System

The Interpretation of Seismogenic Fault of the Maduo Mw 7.3 Earthquake, Qinghai Based on Remote Sensing Images—A Branch of the East Kunlun Fault System

Tools to analyse misleading kinematic interpretations of faults offsetting inclined or folded surfaces: Applications to Asturian Basin (NW Iberian Peninsula) examples

When working with geological maps and cross-sections, and no kinematic criteria on fault motion are available, we usually classify the type of fault (dip-slip, strike-slip, oblique) according to the observed separation of planar markers, usually bedding. Although in many cases the separation agrees with fault slip, and therefore, the deduced type of fault is correct, these deductions can be completely wrong in certain circumstances. Amongst other parameters, the observed separation depends on the angular relationship between the cut-off line of the planar marker on the fault plane and the slip vector. In this paper, we elaborate on this relationship and present a tool to facilitate classifications of faults whose motion does not involve rotation of the fault blocks, and cut and offset previously tilted or folded surfaces. This tool consists of graphs that predict how the observed separation will be in cross sections and maps. In addition, the influence of different parameters on the magnitude of the separation in a geological section across a fault is discussed. The validity of the presented tool is demonstrated through its application to two field examples of strike-slip faults offsetting inclined layers and a fold train in the Asturian Basin. Our graphs can be used to improve geological interpretations, in a predictive way when constructing geological maps and cross-sections, and to decipher the existence of layers tilted or folded before fault development.

The near-surface structure in the area of the Børglum fault, Sorgenfrei-Tornquist Zone, northern Denmark: Implications for fault kinematics, timing of fault activity and fault control on tunnel valley formation

Previous studies showed evidence that the Sorgenfrei-Tornquist Zone (STZ) in northern Denmark is seismically active and fuelled the demand for further analyses of the fault structure and its seismic hazard potential. We focus on the area of the Børglum fault, which represents the northern boundary fault of the STZ and extends at least 250 km from the western Baltic Sea across Jutland into the southeastern North Sea. The Børglum fault has previously been interpreted to represent a glacially-triggered fault. Two shear-wave reflection seismic sections were acquired to image the near-surface structure in the vicinity of the Børglum fault. Detailed insights into the acquisition, processing and interpretation of the seismic sections will be provided. The seismic sections indicate that the Børglum fault in the study area is not an isolated fault, but most likely represents a complex fault system with a strike-slip component. The fault interpretation is based on systematic vertical reflector offsets, the observation of steeply dipping thin transparent zones that separate the offset reflectors, abrupt lateral changes in the reflector pattern and in some cases, the presence of a fault shadow (a diffuse zone on a seismic section below a fault). This interpretation is mainly based on the identification of positive flower structures on the seismic sections, and supported by the presence of elongated sedimentary mini-basins and topographic depressions in the study area, which are partly interpreted to represent strike-slip basins. A WNW-ESE striking tunnel-valley system is imaged on the seismic sections. The spatial relationship between the faults and the tunnel valley suggests a fault-control on tunnel-valley system formation in this area. The seismic sections indicate that faults in the study area were probably active at the end of the Saalian glaciation (MIS 6). In addition, soft-sediment deformation structures and shear-deformation bands developed in Late Pleniglacial to Lateglacial sediments point to repeated phases of fault activity. The reflector pattern on one of the seismic sections indicates that a major fault nearly reaches the Earth's surface. A step in the topography above the fault probably represents a fault-scarp. Because the ongoing glacial-isostatic adjustment will lead to a change in the stress magnitude in this area, future movements along the faults are possible.

Complex trans-ridge normal faults controlling large earthquakes

Studying faults capable of releasing moderate-to-strong earthquakes is fundamental for seismic hazard studies, especially in a territory that was subject to the strongest peninsular Italy earthquake (1857, Mw 7.1) and hosting the largest European oil field on-land. Fieldwork-based observations in the Campania-Lucania area highlight a SSW-dipping ~ 65 km-long normal-oblique-segmented fault, showing evidence of recent activity and possibly responsible for the 1857 earthquake. It crosses the Maddalena ridge, linking separate Quaternary basins. Two seismic reflection profiles cross the fault trace where it is buried beneath the Val d’Agri Quaternary deposits. Similarities between fault-controlled small basins in the highest portion of the massifs in the study area and the neighboring 1980 Irpinia area (1980 earthquake, Mw 6.9) are interpreted as evidence of trans-ridge fault activity. Kinematic analyses and the stress field inversion provide a N032-trending near-horizontal s3-axis, the same computed in literature for the Irpinia area, highlighting a deviation from the ~N045-axis which characterizes most of the Apennines. This study demonstrates how detailed fieldwork, supported by geophysics and innovative data analysis techniques, can unravel unknown faults while giving a novel interpretation of the trans-ridge faults' style in controlling strong earthquakes, moving away from classical interpretations, and providing a helpful approach in similar contexts worldwide.

Improvement of fault interpretation with seismic attribute analysis of Jeju Basin, offshore southern Korea, East China Sea

Improvement of fault interpretation with seismic attribute analysis of Jeju Basin, offshore southern Korea, East China Sea

Automatic Recognition of Faults in Mining Areas Based on Convolutional Neural Network

Tectonic interpretation is critical to a coal mine’s safe production, and fault interpretation is an essential component of seismic tectonic interpretation. With the increasing necessity for accuracy in fault interpretation in coal mines, it is increasingly challenging to achieve greater accuracy only through traditional fault interpretation. The convolutional neural network (CNN) is a machine learning method established in recent years and it has been widely applied in coal mine fault interpretation because of its powerful feature-learning and classification capabilities. To improve the accuracy and efficiency of fault interpretation in coal mines, an automatic seismic fault identification method based on the convolutional neural network has been developed. Taking a mining area in eastern Yunnan province as an example, the CNN model realized automatic identification of faults with eight seismic attributes as feature inputs, and the model-training parameters were optimized and compared. Ten faults in the area were selected to analyze the prediction effect, and a comparative experiment was done with model structure parameters and training sets. The experimental results indicate that the training parameters have a significant influence on the training time and testing accuracy of the model, while structural parameters and training sets affect the actual prediction effect of the model. By comparison, the fault results predicted by the convolutional neural network are in good agreement with the manual interpretation, and the accuracy of the model is more than 85%, which proves that this method has certain feasibility and provides a new way to shorten the fault interpretation period and improve the interpretation accuracy.

Accelerating core characterization and interpretation through deep learning with an application to legacy data sets

Core descriptions provide an essential element of ground truth to subsurface reservoir characterizations, but adequate core characterization and interpretation require a significant time commitment from geoscientists because they are usually done manually. Moreover, core characterizations and interpretations often are inconsistent because they are made at different times by different people. We have used deep-learning segmentation networks, trained using worldwide data sets labeled by specialists, to infer lithofacies and structural features on core images. These networks are not intended to provide perfect core descriptions, but rather to provide high-level fully digital characterizations that can be quickly checked for quality and manually fixed if needed. This ensures that all available and relevant data can be used and relieves geoscientists from the most repetitive characterization tasks to allow them to focus on more complex and value-adding integration and interpretation. We test the previously trained networks in legacy public data from a UK field as an example. There are more than 1300 m of released core images in the field, together with multiple vintages of fault interpretations, that significantly differ from one another. Integrating core and seismic-scale structural characterizations is critical because the best reservoir facies are the most prone to deform via permeability-reducing processes. Detailed structural characterization and identification of individual structural features achieved through deep learning provide an unprecedented amount of data to highlight areas where the seismic-scale fault interpretations and core-scale structural interpretations are inconsistent, yielding valuable insights to validate fault interpretation.

Fault enhancement comparison among coherence enhancement, probabilistic neural networks, and convolutional neural networks in the Taranaki Basin area, New Zealand

Fault identification is a critical component of seismic interpretation. During the past 25 years, coherence, curvature, and other seismic attributes sensitive to faults improved seismic interpretation, but human interaction is still required to generate a complete fault interpretation. Today, image enhancement of fault-sensitive attributes, multiattribute fault analysis using shallow learning, and deep-learning algorithms based on extensive training and convolutional neural networks (CNNs) are promising fault interpretation workflows. We have compared three workflows to test fault-detection capabilities; these include image enhancement, probabilistic neural networks (PNNs), and CNNs. We compared results to human-interpreted faults as our ground truth for a merged 3D seismic survey acquired in the Taranaki Basin, New Zealand. We extracted fault surfaces from the results of the workflows using them as seed points for an active contour method. Extracted faults are then compared to the human-interpreted surface using the Hausdorff distance. Data conditioning, including spectral balancing and structure-oriented filtering, improved the performance of all three workflows. Although all three approaches produce enhanced fault volumes, we find differences in fault location and different artifacts (mispredicted faults). Because all three methods exhibit “false positive” predictions, the enhanced multispectral coherence method produces faults and stratigraphic edges in the final image, including residual stair-step artifacts. In our implementation, PNN produces many salt-and-pepper artifacts through the resulting image, suggesting that we might need to include better training data or reduce the volume size to reduce the number of relevant classes to obtain an improved classification. The CNN algorithm is trained with synthetic data that provide rapid results, correctly identifying larger faults, but missing smaller faults and, in some cases, misclassifying mass-transport deposits and angular unconformities as being faults.

Fault Recognition Method Based on Attention Mechanism and the 3D-UNet.

Oil and gas reservoirs are of great significance for economic benefits. Faults act as important conduits for transporting hydrocarbons and as essential sealing conditions. The location and morphology of faults reflect changes in the shape of the strata, so fault interpretation of seismic data has been an essential task in oil and gas exploration and development. The traditional fault identification method is time-consuming and inaccurate with large uncertainties. This paper proposed a fault recognition method based on 3D-UNet that added attention mechanisms to a convolutional neural network (CNN). This approach takes advantage of the UNet end-to-end architecture and the attention mechanism to focus on essential areas and suppress irrelevant information, allowing the model to focus on more valuable features. A fault identification network for seismic data was proposed by combining the 3D-UNet architecture with the Squeeze-and-Excitation (SE) attention mechanism. The 3D-UNet architecture comprises two parts: encoding and decoding, and the network architecture realizes end-to-end training. At the same time, SE was used to focus on the advantages of feature channels, further improving the accuracy of the network. The model performance was evaluated using the synthetic dataset provided by Wu. Experimental results show that the proposed model has better prediction performance in terms of accuracy, better recognition continuity, and richer fault detail.

Fault Interpretation Based on Similarity and Curvature Attributes in F3 Netherlands

F3 Southern North Sea Netherlands Basin field is an oil and gas field that has a complex fault structure and stratigraphy due to compressional tectonic movements during the Late Cretaceous and Tertiary eras. Information on the presence of faults can act as a trap or migration which then greatly affects oil and gas production and the injection process. A seismic attribute analysis of similarity and curvature was used to interpret the presence of fault structures to obtain information on the presence of subsurface faults in the F3 Netherlands field. The interpretation of the similarity attribute calculation results shows that the faults are marked with a minimum similarity value of 0.77, 0.68, and 0.66 at the time slices Z = 400ms, 1700ms, and 1800ms. The maximum value of most post-positive curvature represents the presence of an ascending fault block and the maximum value of the most negative curvature represents the presence of a descending fault block at the same time incision. Based on the similarity and curvature attribute calculations, it can be interpreted that the major faults in Top Zechstein to Upper Pliocene and the presence of successive faults on Top Zechstein to Base Lower Cretaceous are faults due to the formation of the Zechstein salt dome intrusion

Improving seismic fault mapping through data conditioning using a pre-trained deep convolutional neural network: A case study on Groningen field

Seismic fault interpretation is a crucial and indispensable step in reservoir exploration that requires substantial time. As a result, much research has been dedicated to applying deep learning in this venture. Deep learning has shown significant progress in the identification of seismic faults. However, its applicability has been hindered given the lack of appropriate labelled fault data and poor seismic imaging. The deep convolutional neural network (DCNN) employed in this study is a cutting-edge deep learning technique for image improvement and identification.In this study, detecting seismic faults and improving seismic imaging using a pre-trained DCNN is proposed using the Groningen field that has complexity in accurately imaging sub-salt geological structures as a case study. The presence of salt in the field causes wave attenuation. The fault mapping procedure is considered a segmentation of the 3D seismic problem and trains an encoder-decoder architecture, using a Deep Residual U-net, to generate a fault probability volume. A decent fault prediction result is achieved on the Groningen seismic volume. The next step of this study involved seismic data conditioning, where DCNN is applied to denoise volumes to improve visualisation, significantly below the salt structure. The DCNN, when used to improve seismic imaging, achieved a signal-to-noise ratio (SNR) of 30.2, which is almost quadruple that of the original volume. Faults were mapped on the conditioned volume using DCNN, resulting in an improved seismic fault probability volume. This technique achieved distinctively interpreted faults illustrating the significant improvements DCNN brings to the seismic imaging process. In interpreting new seismic volumes with poor imaging and low signal-to-noise ratio, caused by a change in seismic frequency and amplitude propagating through an attenuating medium like the Groningen field, researchers and geophysicists may apply the DCNN for volume conditioning before mapping seismic faults using neural networks.

Structural styles and linkage evolution in the middle segment of a strike-slip fault: A case from the Tarim Basin, NW China

The structural geometric and kinematic analysis of the Shunbei No. 5 fault zone (fault SB5) in the Tarim Basin was conducted based on the fault interpretation on the three-dimensional (3D) seismic sections and coherence slices of several seismic reflecting surfaces in the study area. The fault SB5 evolution model was recovered using the fault pattern analyses and palaeostress reconstruction, which required factors like the width of the damage zone, maximum throw of the main fault, vertical separation, and the shear crack angle along the fault zone. The results show that: (1) Four tectonic layers in the vertical direction are identified according to the difference of structure style, divided by the top and bottom of the Cambrian salt rocks, and the top surface of the Ordovician carbonate. (2) Strike-slip faults in the study area are the middle segment of the large strike-slip fault zone composed of major faults and overlap zones; 11 stepovers in the study area can be classified into three types according to their geometric characteristics. Strain concentration appears on the overlap zone under the continuous shear stress field and shows high activity. Fault SB5 has undergone three major evolutionary stages: the transpressional stage in the Middle Ordovician, the dextral shearing stage in the Early Silurian, and the final evolutionary sinistral process in the Late Devonian.

Machine-learning assisted interpretation: Integrated fault prediction and extraction case study from the Groningen gas field, Netherlands

Seismic fault detection using machine-learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine-learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and derisking activities. Efforts to automate the detection and extraction of geologic features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine-learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, northeast Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D prestack depth migrated seismic data set. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geologic context to the interpreter and input to dependent workflows more efficiently.

Alteration Characteristics and Precious Metal Availability in Gunung Gembes &amp; Surroundings, Jeruk Village, Pacitan Regency, East Java Province

The research area is located in the area of Mount Gembes, Jeruk Village in Pacitan (East Java). This study aims to determine the characteristic and temperature in the formation of alteration which refers to petrography with the help of samples taken in the study area. The petrographic observations carried out were thin section observations which were then analyzed for the mineral content contained in the sample rock. After knowing the minerals contained in the sample rock, it is then entered into the mineral formation temperature diagram (White &amp; Hedenquist, 1995). The method used in this study includes primary data in the form of geological observations such as lithology, geological structure geomorphology, environmental geology and alteration observations and combined with secondary data. Alteration observations in this study were carried out descriptively (megascopic) and subjective (microscopic). The geomorphic unit that works is of volcanic origin, with high hilly sub-units of porphyry dacite intrusion (volcanic neck). Geological structures that develop in the study area are faults, namely the left horizontal fault of Jeruk 1, the left horizontal fault of Jeruk 2 and the interpretation of the oranges right horizontal fault. Geological resources or sources found in the research area are mineral potential resources that are economical, and negative sources are in the form of mass movement of debris flow type. The Zonation and the type of alteration that developed in the research area are the clay alteration zone and silicification. The clay zone is formed at a temperature &gt;100⁰C-220⁰C with a pH of 3-5 and a silicification zone is formed at a temperature&gt;100⁰C-300⁰C with a pH &lt;2 which is directly controlled by the faults found in the study site.

3D seismic interpretation and prospect identification of the dembe field offshore, Niger delta, Nigeria

3D seismic interpretation and prospect evaluation has been carried out in a hydrocarbon bearing field, standard method of seismic interpretation which includes fault interpretation, well-seismic tie, and horizon mapping were carried out with the aid of seismic attributes such as variance edge and root mean square attributes. Well-seismic-tie established a reasonable tie and indicated that the identified reservoirs were of low impedance as the top of this reservoirs coincided with the troughs. TWT vs Depth plot showed a linear trend which aided in creating a velocity model for the time-depth conversion. Fault mapping revealed that the area was divided into 8 blocks by regionally extensive faults associated with several minor faults which are normal and listric in geometry. Petrophysical analysis showed that the reservoirs has an average porosity of 0.24, water saturation of 0.35, and net-to-gross of 0.78 which are favourable reservoir conditions. A lead Dembe-3, and 2 prospects Dembe-1 and Dembe-2 were identified. Volumetric estimation shows that the prospects boasts of a combined petroleum reserve of about 134 MMSTB of recoverable oil and about 167 BCF of recoverable gas. The results from this study show that, away from currently producing zones of the Dembe field, additional lead and prospects exist which could be further explored to optimize hydrocarbon production in the field. Â

Practice and theoretical and technical progress in exploration and development of Shunbei ultra-deep carbonate oil and gas field, Tarim Basin, NW China

In this review on the exploration and development process of the Shunbei ultra-deep carbonate oil and gas field in the Tarim Basin, the progress of exploration and development technologies during the National 13th Five-Year Plan of China has been summarized systematically, giving important guidance for the exploration and development of ultra-deep marine carbonate reservoirs in China and abroad. Through analyzing the primary geological factors of “hydrocarbon generation-reservoir formation-hydrocarbon accumulation” of ancient and superposed basin comprehensively and dynamically, we point out that because the Lower Cambrian Yuertusi Formation high-quality source rocks have been located in a low-temperature environment for a long time, they were capable of generating hydrocarbon continuously in late stage, providing ideal geological conditions for massive liquid hydrocarbon accumulation in ultra-deep layers. In addition, strike-slip faults developed in tectonically stable areas have strong control on reservoir formation and hydrocarbon accumulation in this region. With these understandings, the exploration focus shifted from the two paleo-uplifts located in the north and the south to the Shuntuoguole lower uplift located in between and achieved major hydrocarbon discoveries. Through continuing improvement of seismic exploration technologies for ultra-deep carbonates in desert, integrated technologies including seismic acquisition in ultra-deep carbonates, seismic imaging of strike-slip faults and the associated cavity-fracture systems, detailed structural interpretation of strike-slip faults, characterization and quantitative description of fault-controlled cavities and fractures, description of fault-controlled traps and target optimization have been established. Geology-engineering integration including well trajectory optimization, high efficiency drilling, completion and reservoir reformation technologies has provided important support for exploration and development of the Shunbei oil and gas field.