Gas Reservoir Identification Research Articles

Gas Reservoir Identification Based on Nonstationary and Anisotropic FFT-MA Stochastic Modeling

Gas Reservoir Identification Based on Nonstationary and Anisotropic FFT-MA Stochastic Modeling

An Intelligent Approach for Gas Reservoir Identification and Structural Evaluation by ANN and Viterbi Algorithm—A Case Study From the Xujiahe Formation, Western Sichuan Depression, China

Gas reservoir identification using seismic data has become a major focus of geophysical exploration. This study presents a gas reservoir identification and structural evaluation method using artificial neural networks (ANNs) and the Viterbi algorithm to improve processing efficiency and evaluate gas reservoir structural control. Initial identification was conducted using deep neural networks (DNNs). Composite seismic attributes sensitive to the multi-component seismic response characteristics of gas reservoirs were obtained. Subsequently, a model expansion dataset and network hyperparameter optimization strategy were employed to assess the optimal DNN model for ReLUactivation with nine hidden layers (3–5–7–7–7–9–9–11–11–11–1). The training model was run with the three composite attributes as input to predict the gas-bearing probability distribution. Considering the importance of evaluating geological structural characteristics, an automatic horizon tracking method using the Viterbi algorithm was proposed to evaluate the structural factors of gas reservoirs. Finally, the ANN-based gas reservoir identification results were comprehensively evaluated based on structural characteristics, thus, reducing the uncertainty, or multiple solutions, predicted by mathematical methods. This scheme was successfully applied to assess synthetic and real data, demonstrating the consistency between the predicted gas reservoir areas and true situation. The effective implementation of this scheme improves processing efficiency and provides a new way to shorten the exploration cycle of a gas reservoir.

Correlation for predicting bubble point pressure for 22.3≤°API≥45 crude oils: A white-box machine learning approach

Bubble point pressure (BPP) is a key parameter for oil and gas reservoir identification, characterization, and management. An accurate correlation of this property with the evolving digital technology of machine learning, in the absence of experimental PVT analysis, serves as guidance in the development and recovery of reservoir fluids. In this study, a predictive BPP correlation was derived by intrinsically linearizing a nonlinear multiple regression, with the best coefficients (global minimum) extracted using White-box (Linear Regression, Ridge Regression, and Lasso Regression) Machine Learning models. The new correlation was developed, validated, and tested using 314 measured PVT data points from the Niger Delta Region. The data were subdivided into four classes: extra-light crude for API > 45, light crude for 31.1 < API ≤ 45, medium crude for 22.3 < API ≤ 31.1, and heavy crude for API ≤ 22.3. Statistical evaluation metrics such as root mean squared error, average absolute relative error, and average relative error were employed to compare the performance of the new correlation with the existing empirical ones. Results showed that the new BPP correlation developed by White-box Linear Regression outperformed the other White box (Ridge Regression and Lasso Regression) and other existing BPP empirical models. Taking advantage of emerging data-driven and machine learning as BPP predictive model is effective and efficient in reservoir fluids analysis.

A three-parameter W transform and its application to gas reservoir identification

Time-frequency analysis (TFA) can reveal geologic information from seismic reflection data and has been widely used in characterizing geologic bodies, reservoir identification, and gas detection. To improve the energy concentration of TFA, we have developed an extension of the W transform (WT) which incorporates a time-varying Gaussian window that adaptively changes according to the characteristics of the seismic data. The new transform addresses a perceived defect in the original WT where the time-frequency amplitude peak can split at the dominant frequency of the signal. The three parameters of the transform are chosen through an optimization procedure that relies on the concentration measurement and Rényi entropy. We illustrate that our transform overcomes the dominant frequency splitting artifact of the WT and improves the time-frequency resolution by using synthetic data and field data from the Zhongjiang gas field in western Sichuan, China.

A combined method for gas-bearing layer identification in a complex sandstone reservoir

Langgu Depression is a mature oil and gas exploration area with complicated lithological and physical properties. The varying formation fluid, low-resistivity hydrocarbon-bearing reservoirs, and non-uniform logging series greatly increase the difficulty of gas reservoir identification. The Monte Carlo method is employed to simulate the neutron–gamma logging responses to gas saturation and the influential factors. According to the result, a new gas identification chart eliminating the influence of porosity and formation water salinity is proposed to identify gas reservoirs in the old wells. At the same time, a fluid factor extracted from array acoustic logging and core measurement data is sensitive to the development of gas-bearing layers and useful for the identification of gas reservoirs in the new wells with array acoustic logging. The field examples show that the new combined method greatly improves the ability to identify gas-bearing layers and works well in old well reexamination and new well interpretation.

Unsupervised-learning based self-organizing neural network using multi-component seismic data: Application to Xujiahe tight-sand gas reservoir in China

The tight sandstone reservoirs of the Xujiahe Formation of the Western Sichuan Basin Depression typically exhibit characteristics such as low porosity, low permeability, and strong heterogeneity, resulting in weak seismic response differences between gas and water. In present study, we developed an unsupervised learning gas reservoir prediction method using a self-organizing neural network (SOM) to identify and predict tight-sand gas reservoirs in areas with few or no wells. First, we extracted seismic attributes data and obtained multi-component seismic composite attributes. Then, the multi-component composite attributes were fed into the SOM network for training, obtaining the gas reservoir prediction results. Simultaneously, the designed SOM prediction scheme was also applied to PP-wave and PS-wave seismic data respectively for gas reservoir identification and prediction. The prediction results of the multi-component composite attributes exhibited good correspondence with available gas drilling information, indicating their unique merits in resolving issues pertaining to the deep tight gas sandstone reservoir identification. Finally, based on geological data and drilling information, we evaluated the effectiveness of the prediction results of proposed methods, and predicted favorable exploration areas. Effective implementation contributes substantially to tight-sand gas reservoir identification and prediction in less or even no well area. • Unsupervised learning-based self-organizing neural network. • Delineate a tight-sand gas reservoir using multi-component seismic data. • Gas reservoir identification and prediction in few wells or even no well area.

Basis Matching Chirplet Transform and Its Application in Tight Sandstone Gas Reservoir Identification

Basis Matching Chirplet Transform and Its Application in Tight Sandstone Gas Reservoir Identification

Synchrosqueezing Polynomial Chirplet Transform and Its Application in Tight Sandstone Gas Reservoir Identification

Polynomial chirplet transform (PCT) can effectively characterize the instantaneous frequency (IF) of mono-mode frequency-modulated (FM) signal, but it is unsuitable to analyze multimode signals. Here, we propose synchrosqueezing PCT (SPCT) for amplitude-modulated (AM) and FM signals with multimode, and apply it to tight sandstone gas reservoirs. First, a multikernel operator is constructed to concentrate the energy near the IF of each mode, and then an IF estimator is derived to further reassign the energy to the corresponding IF ridge. This not only achieves a highly energy-concentrated time-frequency representation, but also retains its invertibility. The synthetic signal is employed to validate the effectiveness of the SPCT. The application on field seismic data also demonstrates that the SPCT can effectively identify tight sandstone gas reservoirs.

Research on recognition of gas saturation in sandstone reservoir based on capture mode

Pulsed neutron technology has been widely used to monitor the remaining oil saturation of the formation. In order to solve the problem of the remaining oil saturation and porosity of the reservoir after casing, and to study the condition of the reservoir after water flooding, the pulsed neutron logging tool with the same technology has been recommended and used in China. Most of these instruments have many measurement modes such as inelastic, capture, carbon–oxygen ratio, and gas-view, and these can measure a large amount of stratum information. By studying the influencing factors of the capture mode in sandstone reservoirs, it can be used to research the methods of gas reservoir identification and quantitatively evaluate the gas saturation. This paper studies the detection principles, modes, advantages, and disadvantages of pulsed neutron logging tools. The principle of gas recognition is analyzed in the capture mode, and the formation model is established based on the Monte Carlo method. The common influencing factors are studied, and the sensitivity of detection is also analyzed. It realizes the identification of gas layers by the combined detector, as well as the calculation of gas saturation in comprehensive logging interpretation. The results show that the source distance of the detector affects the detection sensitivity, and the sensitivity of the near-ultra-far detector combination is better. As the porosity increases, the sensitivity of the detector increases, and the capture count ratio also increases significantly. Based on the same pore conditions, the higher the gas saturation, the lower is the capture gamma ratio. The shale content is less sensitive to the capture gamma count ratio, and it can still be concluded that the higher the mud content, the lower the capture count ratio. The conclusions can be used in the research method of gas formation identification and can also be used to accurately explain and quantitatively analyze the gas saturation of the formation.

Multisynchrosqueezing Generalized S-Transform and Its Application in Tight Sandstone Gas Reservoir Identification

Synchrosqueezing transform (SST) is a high-resolution time-frequency (TF) analysis (TFA) approach for seismic spectral anomaly detection. Here, a novel method called multisynchrosqueezing generalized S-transform (GST) is proposed and applied for the identification of tight sandstone gas reservoirs. In this method, a signal model named the Gaussian-Modulated Signal Model (GMSM) is introduced to estimate the instantaneous frequency (IF) of the signal in the GST’s spectrum. Then, an iterative algorithm constantly approximating IF is constructed to provide a highly energy-concentrated TF representation while allowing for signal reconstruction. Compared to some advanced TFA methods, the proposed method has better energy-concentrated performance due to an accurate estimate of the IF. A simulated signal and field data are employed to verify the effectiveness of the proposed method. It is concluded that the proposed method has great potential as a TFA technique for identifying tight sandstone gas reservoirs.

Seismic quality factor estimation using prestack seismic gathers: A simulated annealing approach

Fluid movement and grain boundary friction are the two main factors affecting the anelastic attenuation of seismic data. The quality factor ([Formula: see text]) quantifies the degree of anelastic attenuation and is commonly used in assisting the identification of gas reservoirs. We can accurately compute [Formula: see text] if we obtain the accurate amplitude spectrum of seismic wavelets at refereed and at target time indexes of the seismic profile. However, it is very difficult to obtain the accurate wavelets at the referred and target time indexes. Instead, we usually compare the changes of the amplitude spectrum of refereed and target seismic waveform. The seismic waveform is the convolution result between the seismic wavelet and the reflectivity series. Thus, the reflectivity series would affect our [Formula: see text] computation. We have assumed that the changes of the reflectivity series are negligible within one common-midpoint gather. Then the effect of relativity to the seismic spectrum is the same for the same seismic waveform at different offsets. The seismic waveforms at near offsets are treated as referred seismic signals, and the seismic waveforms at medium and far offsets are regarded as target seismic signals. We use simulated annealing to simultaneously obtain [Formula: see text] values at the entire time index of seismic traces. The method is applied to synthetic and real seismic data to demonstrate its validity and effectiveness. Both applications illustrate the effectiveness of the method in estimating seismic attenuation.

The research of Junggar Basin ppw block gas reservoir identification method

Through the deep understanding of the reservoir characteristics of PPW block and the in-depth analysis of the gas layer identification method, this paper proposes a series of effective methods for identifying the characteristics of the local gas layer. Combining different logging methods and selecting the proper logging data processing method to amplify the atmosphere contains gas information, the air-layer recognition rate can be improved, which plays a fatal role for the exploration and development of gas reservoirs in PPW block.

Identification of gas reservoirs and determination of their parameters by the combination of radioactive logging methods

The paper proposes a new approach for using of combination of radioactivity logging, which allows determining a set of gas reservoir parameters (nature of saturation, true porosity, gas saturation coefficient) taking into account influence of the pressure and temperature conditions of occurrence. Analysis of the ways of averaging the neutron-apparent porosity and the density-apparent porosity for obtaining the true porosity was made. Method of determination of gas saturation, which uses the same combination of radioactivity logging as in determining the true porosity of gas reservoirs, was developed. The results presented in the paper, were obtained over a wide range of depth (up to 10 km). Application of developed approaches for determination of petro-physical parameters of gas reservoirs is demonstrated by the example of cased coalbed methane well.

Geophysical Evaluation Technology for Shale Gas Reservoir: A Case Study in Silurian of Changning Area in Sichuan Basin

Shale gas belongs to unconventional natural gas; it has many reservoir evaluation parameters hard to evaluate. Accurate calculation of parameters and comprehensive evaluation of favorable blocks are critical for geological evaluation, well deployment and reservoir reconstruction of shale gas. After research of Silurian Longmaxi Formation in Changning area in Sichuan Basin a petro physics model for shale gas reservoir is established through a large number of core data analysis and a calculation method for reservoir critical parameters is established through logging data. Based on logging results, the sensitivity analysis indicates that Vp/Vs is the most optimally sensitive parameter for shale gas reservoir identification and evaluation. The thickness, the total organic carbon content, the total gas content and the brittleness index of shale gas reservoir are realized by pre-stack simultaneous inversion technology. The reservoir classification standard is established, then the comprehensive evaluation for shale gas is achieved. There is a high consistency between evaluation results and actual drilling results, so the geophysical evaluation technology for shale gas reservoir can effectively guide well deployment and horizontal well drilling design and can provide technical support for efficient development of shale gas.

Gas reservoir identification by seismic AVO attributes on fluid substitution

Traditionally, fluid substitutions are often conducted on log data for calculating reservoir elastic properties with different pore fluids. Their corresponding seismic responses are computed by seismic forward modeling for direct gas reservoir identification. The workflow provides us with the information about reservoir and seismic but just at the well. For real reservoirs, the reservoir parameters such as porosity, clay content, and thickness vary with location. So the information from traditional fluid substitution just at the well is limited. By assuming a rock physics model linking the elastic properties to porosity and mineralogy, we conducted seismic forward modeling and AVO attributes computation on a three-layer earth model with varying porosity, clay content, and formation thickness. Then we analyzed the relations between AVO attributes at wet reservoirs and those at the same but gas reservoirs. We arrived at their linear relations within the assumption framework used in the forward modeling. Their linear relations make it possible to directly conduct fluid substitution on seismic AVO attributes. Finally, we applied these linear relations for fluid substitution on seismic data and identified gas reservoirs by the cross-plot between the AVO attributes from seismic data and those from seismic data after direct fluid substitution.