An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Application of 9-component S-wave 3D seismic data to study sedimentary facies and reservoirs in a biogasbearing area:A case study on the Pleistocene Qigequan Formation in Taidong area,Sanhu Depression,Qaidam Basin,NW China

To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.

Velocity structure in the South Yellow Sea basin based on first-arrival tomography of wide-angle seismic data and its geological implications

The South Yellow Sea basin is filled with Mesozoic-Cenozoic continental sediments overlying pre-Palaeozoic and Mesozoic-Palaeozoic marine sediments.Conventional multi-channel seismic data cannot describe the velocity structure of the marine residual basin in detail,leading to the lack of a deeper understanding of the distribution and lithology owing to strong energy shielding on the top interface of marine sediments.In this study,we present seismic tomography data from ocean bottom seismographs that describe the NEE-trending velocity distributions of the basin.The results indicate that strong velocity variations occur at shallow crustal levels.Horizontal velocity bodies show good correlation with surface geological features,and multi-layer features exist in the vertical velocity framework(depth:0–10 km).The analyses of the velocity model,gravity data,magnetic data,multichannel seismic profiles,and drilling data showed that high-velocity anomalies(>6.5 km/s)of small(thickness:1–2 km)and large(thickness:>5 km)scales were caused by igneous complexes in the multi-layer structure,which were active during the Palaeogene.Possible locations of good Mesozoic and Palaeozoic marine strata are limited to the Central Uplift and the western part of the Northern Depression along the wide-angle ocean bottom seismograph array.Following the Indosinian movement,a strong compression existed in the Northern Depression during the extensional phase that caused the formation of folds in the middle of the survey line.This study is useful for reconstructing the regional tectonic evolution and delineating the distribution of the marine residual basin in the South Yellow Sea basin.

Microseismic source location using the Log-Cosh function and distant sensor-removed P-wave arrival data

Source location is the core foundation of microseismic monitoring.To date,commonly used location methods have usually been based on the ray-tracing travel-time technique,which generally adopts an L1 or L2 norm to establish the location objective function.However,the L1 norm usually achieves low location accuracy,whereas the L2 norm is easily affected by large P-wave arrival-time picking errors.In addition,traditional location methods may be affected by the initial iteration point used to find a local optimum location.Furthermore,the P-wave arrival-time data that have travelled long distances are usually poor in quality.To address these problems,this paper presents a microseismic source location method using the Log-Cosh function and distant sensor-removed P-wave arrival data.Its basic principles are as follows:First,the source location objective function is established using the Log-Cosh function.This function has the stability of the L1 norm and location accuracy of the L2 norm.Then,multiple initial points are generated randomly in the mining area,and the established Log-Cosh location objective function is used to obtain multiple corresponding location results.The average value of the 50 location points with the largest data field potential values is treated as the initial location result.Next,the P-wave travel times from the initial location result to triggered sensors are calculated,and then the P-wave arrival data with travel times exceeding 0.2 s are removed.Finally,the aforementioned location steps are repeated with the denoised P-wave arrival dataset to obtain a high-precision location result.Two synthetic events and eight blasting events from the Yongshaba mine,China,were used to test the proposed method.Regardless of whether the P-wave arrival data with long travel times were eliminated,the location error of the proposed method was smaller than that of the L1/L2 norm and trigger-time-based location method(TT1/TT2 method).Furthermore,after eliminating the Pwave arrival data with long travel distances,the location accuracy of these three location methods increased,indicating that the proposed location method has good application prospects.

Tectonics of the Solomon Sea Basin from Vertical Gravity Gradient and Seismic Data

The Solomon Sea Basin is a Cenozoic back-arc spreading basin within the convergence system of the Pacific and Indo-Australian plates.Against the background of subduction polarity reversal,the current Solomon Sea Basin gradually formed a rhombic morphology with the subduction of the basin along the New Britain Trench and the Trobriand Trough.By analyzing the vertical gravity gradient,natural earthquake and seismic reflection data,this study determines the structural characteristics of the Solomon Sea Basin.It was found that the tectonics of the basin are characterized by the original expansion structure within the central part in addition to the structure induced by the latest subduction along the basin margin.The original spreading structure of the basin presented an east–west linear graben and horst controlled by normal faults during the basin expansion period.As a result of the subduction and slab-pull of the Solomon Sea Basin,extensional structure belts parallel to the New Britain Trench formed along the basin margin.

Sparse Seismic Data Reconstruction Based on a Convolutional Neural Network Algorithm

At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achieving uniform and intensive acquisition,which makes complete seismic data collection impossible.Therefore,data reconstruction is required in the processing link to ensure imaging accuracy.Deep learning,as a new field in rapid development,presents clear advantages in feature extraction and modeling.In this study,the convolutional neural network deep learning algorithm is applied to seismic data reconstruction.Based on the convolutional neural network algorithm and combined with the characteristics of seismic data acquisition,two training strategies of supervised and unsupervised learning are designed to reconstruct sparse acquisition seismic records.First,a supervised learning strategy is proposed for labeled data,wherein the complete seismic data are segmented as the input of the training set and are randomly sampled before each training,thereby increasing the number of samples and the richness of features.Second,an unsupervised learning strategy based on large samples is proposed for unlabeled data,and the rolling segmentation method is used to update(pseudo)labels and training parameters in the training process.Through the reconstruction test of simulated and actual data,the deep learning algorithm based on a convolutional neural network shows better reconstruction quality and higher accuracy than compressed sensing based on Curvelet transform.

Paleostress Reconstruction from 3D Seismic Data and Slip Tendency in the Northern Slope Area of the Bongor Basin, Southwestern Chad

Paleostress plays a significant role in controlling the formation, accumulation, and distribution of reservoirs, and this could be an important factor in controlling the production of hydrocarbons from the unconventional reservoirs. In this study, we will use 3D seismic reflection data to perform the slip-tendency-based stress inversion to determine the stress field in the basement of the northern slope area in the Bongor Basin. The dataset for this technique is easily available in the oil and gas companies. The stress inversion results from the basement of the northern slope area of Bongor basin show that the maximum principal stress axis (σ1) is oriented vertically, the intermediate principal stress axis (σ2) is oriented N18° and the minimum principal stress axis (σ3) is oriented N105°, and σ2/σ1 = 0.60 and σ3/σ1 = 0.29. The findings of this paper provide significant information to understand the fault reactivation at the critical stage of hydrocarbon accumulation and the regional tectonic evolution.

Data processing of the Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system and application to South China Sea data

The Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system was designed by the First Institute of Oceanography,Ministry of Natural Resources(FIO,MNR).The system is mainly composed of a plasma spark source(source level:216 dB,main frequency:750 Hz,frequency bandwidth:150-1200 Hz)and a towed hydrophone streamer with 48 channels.Because the source and the towed hydrophone streamer are constantly moving according to the towing configuration,the accurate positioning of the towing hydrophone array and the moveout correction of deep-towed multichannel seismic data processing before imaging are challenging.Initially,according to the characteristics of the system and the towing streamer shape in deep water,travel-time positioning method was used to construct the hydrophone streamer shape,and the results were corrected by using the polynomial curve fitting method.Then,a new data-processing workflow for Kuiyang-ST2000 system data was introduced,mainly including float datum setting,residual static correction,phase-based moveout correction,which allows the imaging algorithms of conventional marine seismic data processing to extend to deep-towed seismic data.We successfully applied the Kuiyang-ST2000 system and methodology of data processing to a gas hydrate survey of the Qiongdongnan and Shenhu areas in the South China Sea,and the results show that the profile has very high vertical and lateral resolutions(0.5 m and 8 m,respectively),which can provide full and accurate details of gas hydrate-related and geohazard sedimentary and structural features in the South China Sea.

A regression approach for seismic first-break picking

The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.

A deep learning framework for suppressing prestack seismic random noise without noise-free labels

Random noise attenuation is significant in seismic data processing.Supervised deep learning-based denoising methods have been widely developed and applied in recent years.In practice,it is often time-consuming and laborious to obtain noise-free data for supervised learning.Therefore,we propose a novel deep learning framework to denoise prestack seismic data without clean labels,which trains a high-resolution residual neural network(SRResnet)with noisy data for input and the same valid data with different noise for output.Since valid signals in noisy sample pairs are spatially correlated and random noise is spatially independent and unpredictable,the model can learn the features of valid data while suppressing random noise.Noisy data targets are generated by a simple conventional method without fine-tuning parameters.The initial estimates allow signal or noise leakage as the network does not require clean labels.The Monte Carlo strategy is applied to select training patches for increasing valid patches and expanding training datasets.Transfer learning is used to improve the generalization of real data processing.The synthetic and real data tests perform better than the commonly used state-of-the-art denoising methods.

Major methods of seismic anisotropy

Seismic anisotropy reveals that seismic wave velocity, amplitude, and other physical properties show variations in different directions, which can be divided into lattice-preferred orientation(LPO) and shape-preferred orientation(SPO) according to its physical mechanisms. The main methods for studying seismic anisotropy include shearwave splitting analysis, P-wave travel time inversion and surface-wave tomography, etc. There are some differences and correlations among these methods. Seismic anisotropy is an important way to reveal the dynamic processes of crust-mantle evolution, and it is significant for monitoring crustal stress changes and improve seismic exploration studies. With the help of long-term observation, the application of machine learning techniques and combining inversion based on multiple phases would become potential developments in seismic anisotropy studies. This may improve the understanding of complex seismic anisotropic models, such as multiple layers anisotropy with an oblique axis of symmetry.

Angular unconformity in Pennsylvanian strata from 3-D seismic interpretation,Goldsmith Field,West Texas

The Pennsylvanian unconformity,which is a detrital surface,separates the beds of the Permian-aged strata from the Lower Paleozoic in the Central Basin Platform.Seismic data interpretation indicates that the unconformity is an angular unconformity,overlying multiple normal faults,and accompanied with a thrust fault which maximizes the region's structural complexity.Additionally,the Pennsylvanian angular unconformity creates pinch-outs between the beds above and below.We computed the spectral decomposition and reflector convergence attributes and analyzed them to characterize the angular unconformity and faults.The spectral decomposition attribute divides the broadband seismic data into different spectral bands to resolve thin beds and show thickness variations.In contrast,the reflector convergence attribute highlights the location and direction of the pinch-outs as they dip south at angles between 2° and 6°.After reviewing findings from RGB blending of the spectrally decomposed frequencies along the Pennsylvanian unconformity,we observed channel-like features and multiple linear bands in addition to the faults and pinch-outs.It can be inferred that the identified linear bands could be the result of different lithologies associated with the tilting of the beds,and the faults may possibly influence hydrocarbon migration or act as a flow barrier to entrap hydrocarbon accumulation.The identification of this angular unconformity and the associated features in the study area are vital for the following reasons:1)the unconformity surface represents a natural stratigraphic boundary;2)the stratigraphic pinch-outs act as fluid flow connectivity boundaries;3)the areal extent of compartmentalized reservoirs'boundaries created by the angular unconformity are better defined;and 4)fault displacements are better understood when planning well locations as faults can be flow barriers,or permeability conduits,depending on facies heterogeneity and/or seal effectiveness of a fault,which can affect hydrocarbon production.The methodology utilized in this study is a further step in the characterization of reservoirs and can be used to expand our knowledge and obtain more information about the Goldsmith Field.

Irregular seismic data reconstruction based on exponential threshold model of POCS method

Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets （POCS） based image restoration method into the seismic data reconstruction field to interpolate irregularly missing traces. For entire dead traces, we transfer the POCS iteration reconstruction process from the time to frequency domain to save computational cost because forward and reverse Fourier time transforms are not needed. In each iteration, the selection threshold parameter is important for reconstruction efficiency. In this paper, we designed two types of threshold models to reconstruct irregularly missing seismic data. The experimental results show that an exponential threshold can greatly reduce iterations and improve reconstruction efficiency compared to a linear threshold for the same reconstruction result. We also analyze the anti- noise and anti-alias ability of the POCS reconstruction method. Finally, theoretical model tests and real data examples indicate that the proposed method is efficient and applicable.

Separation of P. and SV-wavefields from multi-componen seismic data

In multi-component seismic exploration, the horizontal and vertical components both contain P- and SV-waves. The P- and SV-wavefields in a seismic record can be separated by their horizontal and vertical displacements when upgoing P- and SV-waves arrive at the sea floor. If the sea floor P wave velocity, S wave velocity, and density are known, the separation can be achieved in ther-p domain. The separated wavefields are then transformed to the time domain. A method of separating P- and SV-wavefields is presented in this paper and used to effectively separate P- and SV-wavefields in synthetic and real data. The application to real data shows that this method is feasible and effective. It also can be used for free surface data.

Offshore Fault Geometrics in the Pearl River Estuary,Southeastern China:Evidence from Seismic Reflection Data

The Pearl River Estuary(PRE) is located at the onshore-offshore transition zone between South China and South China Sea Basin, and it is of great significant value in discussing tectonic relationships between South China block and South China Sea block and seismic activities along the offshore active faults in PRE. However, the researches on geometric characteristics of offshore faults in this area are extremely lacking. To investigate the offshore fault distribution and their geometric features in the PRE in greater detail, we acquired thirteen seismic reflection profiles in 2015. Combining the analysis of the seismic reflection and free-air gravity anomaly data, this paper revealed the location, continuity, and geometry of the littoral fault zone and other offshore faults in PRE. The littoral fault zone is composed of the major Dangan Islands fault and several parallel, high-angle, normal faults, which mainly trend northeast to northeast-to-east and dip to the southeast with large displacements. The fault zone is divided into three different segments by the northwest-trending faults. Moreover, the basement depth around Dangan Islands is very shallow, while it suddenly increases along the islands westward and southward. These has resulted in the islands and neighboring areas becoming the places where the stress accumulates easily. The seismogenic pattern of this area is closely related to the comprehensive effect of intersecting faults together with the low velocity layer.

The Analysis of Seismic Data Structure and Oil and Gas Prediction

In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indi- cates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine fades (including fluvial facies, lacustrine fades); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).

The improved ICA algorithm and its application in the seismic data denoising

The field seismic data is disturbed by the interferential information, which has low signal to noise ratio (SNR). That is disadvantage for seismic data interpretation. So it is important to remove the noise of seismic data. Independent component analysis (ICA) can remove most of the noise interference. However, ICA has some defects in noise reduction, because it needs some conditions that seismic data is independent reciprocally for denoising. To solve these defects, this paper proposes an improved ICA algorithm to noise reduction. Through simulation experiments, it can be obtained that the best decomposition levels of the new algorithm is 3. At last, the proposed improved ICA is applied to deal with the actual seismic data. The results show that it can effectively eliminate most of seismic noise such as random noise, linear interference, surface waves, and so on. The improved ICA is not only easy to denoising, but also has excellent mathematical theoretical properties.

Method for Morphological Filtering in Seismic Data Processing

A new method is introduced to suppress the noise in seismic data processing. Based on the subtle difference in shape between the noise and the actual signal, we introduce morphologic filtering into seismic data processing. From the shape and the S/N we can see that the effect of morphologic filtering is superior to other methods like id-value filtering, neighbor average filtering, etc. The SNR of the signal after morphological filtering is comparatively great. In addition, the precision of the seismic data after morphological filtering is high. The characteristics of the actual signal, such as frequency and amplitude, are preserved. We give an example of the real seismic data processing using morphological filtering, in which the actual signal is retained, while the random high intensity noise was removed.

Application of multi-scaled morphology in denoising seismic data

In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic waves; second, we illustrate how to use multi-scaled morphology for seismic data processing using two real examples. The first example demonstrates suppressing the surface waves in pre-stack seismic records using multi-scaled morphology decomposition and reconstitution and the other example demonstrates filtering different interference waves on the seismic record. Multi-scaled morphology filtering separates signal from noise by the detailed differences of the wave shapes. The successful applications suggest that multi-scaled morphology has a promising application in seismic data processing.

Predicting the distribution of reservoirs by applying the method of seismic data structure characteristics： Example from the eighth zone in Tahe Oilfield

Seismic data structure characteristics means the waveform character arranged in the time sequence at discrete data points in each 2-D or 3-D seismic trace. Hydrocarbon prediction using seismic data structure characteristics is a new reservoir prediction technique. When the main pay interval is in carbonate fracture and fissure-cavern type reservoirs with very strong inhomogeneity, there are some difficulties with hydrocarbon prediction. Because of the special geological conditions of the eighth zone in the Tahe oil field, we apply seismic data structure characteristics to hydrocarbon prediction for the Ordovician reservoir in this zone. We divide the area oil zone into favorable and unfavorable blocks. Eighteen well locations were proposed in the favorable oil block, drilled, and recovered higher output of oil and gas.