Reservoir Prediction Research Articles

Criteria and favorable distribution area prediction of Paleogene effective sandstone reservoirs in the Lufeng Sag, Pearl River Mouth Basin

As the focus of conventional oil and gas exploration is changing from shallow to deep layers, the identification of deep effective reservoirs is crucial to exploration and development. In this paper, based on the geological anatomy of oil and gas reservoirs, a new discriminatory criterion and evaluation method for effective reservoirs is proposed in combination with the analysis of reservoir formation dynamics mechanism. The results show that the hydrocarbon properties of the reservoir vary with the ratio of the capillary force between the sandstone reservoir and its surrounding rock. The effective reservoir is discriminated and the reservoir quality is evaluated based on the capillary force and depth of the surrounding media and the sandstone reservoir for adjacent plates. When the capillary force ratio is greater than 0.6, fewer effective reservoirs are developed. The effective reservoir is determined by the capillary force ratio of the sandstone reservoir and the surrounding rock medium to mechanically explain the geological phenomenon that low-porosity reservoirs can also accumulate hydrocarbons. Our findings have significant guiding value for Paleogene oil and gas exploration in the Zhu I depression of Pearl River Mouth Basin. Cited as: Yu, S., Wang, C., Chen, D., Guo, B., Cai, Z., Xu, Z. Criteria and favorable distribution area prediction of Paleogene effective sandstone reservoirs in the Lufeng Sag, Pearl River Mouth Basin. Advances in Geo-Energy Research, 2022, 6(5): 388-401. https://doi.org/10.46690/ager.2022.05.04

Rock Physical Modeling of Tight Sandstones Based on Digital Rocks and Reservoir Porosity Prediction From Seismic Data

Digital rock physics (DRP) has become an important tool to analyze the characteristics of pore structures and minerals and reveal the relationships between microscopic structures and the physical properties of reservoirs. However, it is greatly difficult to upscale the rock physical parameters, such as P-wave velocity, S-wave velocity, and elastic moduli, from DRP to large-scale boreholes and reservoirs. On the other hand, theoretical rock physical modeling can establish the internal relationship between the elastic properties and physical parameters of tight sandstones, which provides a theoretical basis for seismic inversion and seismic forward modeling. Therefore, the combination of digital rock physics and rock physical modeling can guide the identification and evaluation of the gas reservoir’s “sweet spot.” In this study, the CT images are used to analyze the mineral and pore characteristics. After that, the V-R-H model is used to calculate the equivalent elastic moduli of rocks containing only the mineral matrix, and then, the differential equivalent medium (DEM) model is used to obtain the elastic moduli of dry rocks containing minerals and pores. Subsequently, the homogeneous saturation model is used to fill the fluids in the pores and the Gassmann equation is used to calculate the equivalent elastic moduli of the saturated rock of tight sandstones. Rock physical modeling is calibrated, and the reliability of the rock physical model is verified by comparing those with the logging data. Afterward, the empirical relationship of rock porosity established from CT images and rock elastic moduli is obtained, and then, the elastic parameters obtained by seismic data inversion are converted into porosity parameters by using this empirical relationship. Finally, the porosity prediction of large-scale reservoirs in the study area is realized to figure out the distribution of gas reservoirs with high porosity. The results show that the H3b and H3c sections of the study area exhibit higher porosity than H3a. For the H3b reservoir, the northeast and middle areas of the gas field are potential targets since their porosity is larger than that of others, from 10% to 20%. Because of the effects of the provenance from the east direction, the southeast region of the H3c reservoir exhibits higher porosity than others.

Geochemical Characteristics and Their Geological Significance of Lower Cambrian Xiaoerblak Formation in Northwestern Tarim Basin, China

Lower Cambrian Xiaoerblak Formation is one of the major exploration targets in Cambrian pre-salt Tarim Basin; however, the exploration breakthrough is restricted by insufficient understanding of its sedimentary evolution and reservoir genesis. In this paper, based on a systematic description of the outcrop in the Xiaoerblak section, northwestern Tarim Basin, some samples were selected for tests of stable carbon and oxygen isotopic compositions, strontium isotopic composition, order degree, trace and rare earth elements, U-Pb isotopic age and clumped isotope. It is found that the Xiaoerblak Formation mainly develops nine types of dolomites, i.e., laminated microbial dolomite, thrombolite dolomite, stromatolite dolomite, foamy microbial dolomite, grain dolomite, etc. According to the lithofacies associations, it can be divided into three members: Xi 1, Xi 2, and Xi 3, of which member Xi 2 is subdivided into three submembers. The characteristics of lithofacies assemblage formed bottom to top indicate that it can be described as a third-order sequence. The Xiaoerblak Formation was deposited in a nearshore shallow seawater environment characterized by high water salinity and temperature under a warm and humid climate during the Early Cambrian, giving rise to the sedimentary sequence of inner ramp lagoon, subtidal microbial mound shoal and tidal flat in the carbonate ramp setting from bottom to top. Its dolomitization occurred in the penecontemporaneous–shallow burial period when the temperature was relatively low and high-salinity seawater acted as the main dolomitizaiton fluid. The reservoir space mainly comprises primary microbial framework pores and vugs formed by the atmospheric freshwater dissolution. Reservoirs were controlled by lithofacies, high-frequency sequence boundary and early dolomitization. The research results are of great significance for presalt Cambrian lithofacies paleogeographic mapping and reservoir prediction.

Prediction method and application of shale reservoirs core gas content based on machine learning

To improve the recovery of shale gas, it is very important to accurately grasp the gas content of shale reservoirs. Considering the problems of low accuracy, strong local limitation and poor adaptability of seismic data of traditional methods such as empirical formulas and regression fitting. Based on machine learning (ML) algorithms such as support vector regression, decision trees, random forests, BP neural networks and convolutional neural networks, an intelligent prediction method of shale reservoir core gas content based on machine learning was established by using three parameters: P-wave velocity (Vp), S-wave velocity (Vs) and density (RHOB). It was compared with traditional method prediction, core tests and other gas content data, which verified the effectiveness and high-precision characteristics of the method. Additionally, in support vector regression (SVR), decision tree (DT), random forest (RF), BP neural network (BP), convolutional neural network (CNN) and other machine learning algorithms, the support vector regression algorithm was the most stable, robust and accurate. Because it takes the easy-to-obtain “three parameters” as input data and retains the gas content characteristics of core data, it also has strong generalization ability and easy migration advantages, which can be easily extended to gas content prediction of three-dimensional shale reservoirs based on seismic inversion data. The prediction results of this method in the core gas content of the shale reservoir of the Wufeng Longmaxi Formation in the southern Sichuan Basin show that compared with the traditional method, the gas content prediction accuracy based on the machine learning algorithm was higher. Therefore, it can provide method support for shale reservoir target optimization and drilling deployment.

Recurrent autoencoder model for unsupervised seismic facies analysis

Machine learning-based automatic seismic facies analysis has increased significantly over the past few decades. The key is to select the most representative features (such as the commonly used poststack amplitude data or the induced seismic attributes) as the input of the different machine learning algorithms. As an advanced branch of machine learning, deep learning can be used to extract the discriminatively deep features from seismic data similar to those used in image classification. In this study, we havedeveloped an unsupervised seismic facies analysis method by using a recurrent autoencoder model. First, we have constructed and trained an autoencoder architecture combined with long short-term memory-based recurrent operation. Its main aim is to learn the deep discriminative features by taking the windowed poststack seismic data as the input time series data. This type of unsupervised learning takes advantage of the no labeling requirement of seismic data. In addition, the recurrent operation is beneficial in delineating the time-sequential characteristics of seismic data. Second, we have taken the learned features as the input of simple K-means clustering and analyzed the corresponding seismic facies. In other words, the clustering is executed in the learned feature space (learned feature-based clustering). Real data results have demonstrated that our method reveals more details than the original amplitude-based K-means clustering, depending on the cluster calibration. In particular, according to the known natural gamma-ray logs and lithological descriptions of five wells, different amounts of sandstone and mudstone deposit are more accurately discriminated, which is substantially informative in reservoir prediction and hydrocarbon exploration. Furthermore, the estimated average silhouette scores have quantitatively shown the effectiveness of our method.

Acoustic Prediction of a Multilateral-Well Unconventional Reservoir Based on a Hybrid Feature-Enhancement Long Short-Term Memory Neural Network

Due to the complexity of unconventional reservoir measurement, log data acquired are often incomplete, which results in inaccurate formation evaluation and higher operational risks. Common solutions, such as coring, are typically high cost related while not being sufficiently representative. In recent years, neural network has received increasing attention given its strong ability in data prediction. Nevertheless, most neural networks only focus on one specific feature of the selected data, thus prohibiting their prediction accuracy for reservoir logs where data are often dominated by more than one key feature. To address this challenge, a novel multi-channel hybrid Long Short-Term Memory (LSTM) neural network for effective acoustic log prediction is proposed. The network combines Convolutional Neural Network (CNN) and LSTM, where CNN is used to extract spatial features of the logs and LSTM network extracts temporal features with the assistance of an adaptive attention mechanism implemented for key feature recognition. In addition, the strong heterogeneity of unconventional reservoirs also increases the difficulty of prediction. Therefore, according to the characteristics of the unconventional reservoir, we designed three feature enhancement methods to mine the hidden information of logs. To prove the performance of the proposed network, a case study is presented with data acquired from Jimusar Oilfield, one of the largest unconventional reservoirs in China. Four groups of experiments are conducted, and the proposed network is employed for acoustic log prediction. The predicted results are validated against measurement (R2: 92.27%, 91.42%, 93.31%, and 92.03%; RMSE: 3.32%, 3.92%, 3.06%, and 3.53%). The performance of the proposed network is compared to other networks such as CNN, LSTM, CNN-LSTM, and random forest (RF). The comparisons show that the proposed network has the highest accuracy level of prediction, which means it provides an effective approach to complement missing data during complicated unconventional reservoir measurement and, therefore, could be of significant potential in energy exploration.

Multi-scale classification and evaluation of shale reservoirs and ‘sweet spot’ prediction of the second and third members of the Qingshankou Formation in the Songliao Basin based on machine learning

Owing to the unique structure of shale reservoirs intercalated with thin siltstone in the second and third members of the Qingshankou Formation (K1qn2+3) in the north of the Central Depression of the Songliao Basin, it is difficult to objectively predict and evaluate multi-scale (i.e., from the micro to macro scale) physical properties of the reservoir and the ‘sweet spot’ area (the area with the best reservoir quality). Using machine learning, we present herein a new machine learning framework (GCA-CE-MGPK) specific to reservoirs for studying the shale reservoirs in K1qn2+3. According to the results of a high-pressure mercury-injection experiment, organic geochemical analysis and scanning electron microscopy. Through grey correlation analysis, clustering ensemble and the Kriging model combined with macro geological parameters, an efficient, accurate and objective multi-scale evaluation of the shale reservoirs and the prediction of the ‘sweet spot’ area were realised. This method can be used to overcome difficulty in parameter selection, time-consuming classification of a large amount of data and difficulty in macro-scale reservoir prediction in the absence of seismic data with an average accuracy of 82.4%. The reservoir prediction results showed that the Class-I reservoir, the ‘sweet spots’ area, is mainly distributed in the north of the study area with an area of 3.15 × 108 m2; the Class-II reservoir is mainly distributed in the north of the study area with an area of 9.88 × 108 m2; the Class-III shale reservoir is the most widely distributed reservoir type with an area of 4.90 × 109 m2. Overall, compared with K1qn1, K1qn2+3 offers more realistic oil and gas exploration potential and advantages.

High-Frequency Sea-Level Cycle Reconstruction and Vertical Distribution of Carbonate Ramp Shoal Facies Dolomite Reservoir in Gucheng Area, East Tarim Basin

During the sedimentary period of the Ordovician Yingshan Formation, the carbonate platform of the Gucheng area in the Tarim basin was characterized by a distally steepened ramp. Relative sea-level changes exerted a strong influence on the shoal facie dolomite reservoirs of the 3rd Member of the Ordovician Yingshan Formation (the Ying 3 member), sedimented in the context of a shallow water environment on the carbonate ramp. However, previous studies that lacked high-frequency sea-level changes in the Gucheng area prevent further dolomite reservoir characterization. The current work carries out systematic sampling based on the continuous core from the upper and middle parts of the Ying 3 member in two newly drilled exploration wells (GC17 and GC601) and a series of geochemistry analyses, such as C-O isotope, Sr isotope, and rare earth elements (REE), which helps to investigate the features of the shoal facies dolomite reservoir development against high-frequency sea-level changes. With the help of Fischer plots of these two wells, high-density δ13C data (sample interval is about 0.272 m) were merged to construct a comprehensive curve, contributing to characterizing the high-frequency sea-level changes of the upper and middle parts of the Ying 3 member in the Gucheng area and validating the relationship between the pore-vug vertical distribution and high-frequency sea-level changes. Results revealed that the porosity of dolomite reservoirs increased when the high-frequency sea-level fell and decreased when it rose. Furthermore, the karst surface can be found at the top of the upward-shallowing cycle during the high-frequency sea-level falling; the pore-vug reservoirs are concentrated below the karst exposure surface, and porous spaces are more developed closer to the top of the cycle. The high frequency sea-level curve built in this study can be used as a standard for further research of regional sea-levels in the Gucheng area, and this understanding is highly practical in the prediction of shoal facies carbonate reservoir in carbonate ramp.

Petrophysical properties prediction of deep dolomite reservoir considering pore structure

Petrophysical properties prediction of deep dolomite reservoir considering pore structure

Origin and Trace of Hydrothermal Calcites in the Hydrocarbon Reservoirs Intruded by Diabase: Evidence From Strontium and Oxygen Isotopic Proxies

Thermal contact clastic rocks provide a new potential type of hydrocarbon reservoirs for expanding exploration targets. However, the development mechanism of this type of reservoir remains enigmatic. Authigenic hydrothermal carbonates associated with magmatism exert significant controls over physical properties of the contact reservoirs. Therefore, the origin and trace of hydrothermal carbonates are key issues for reservoir development elucidation of thermal contact reservoirs and need to be further investigated. The current study takes the typical hydrocarbon area of thermal contact metasandstone reservoirs from the Funing Formation, northern Slope of Gaoyou sag, as a case study, aiming to unravel the origin and trace of authigenic hydrothermal calcites developed in thermal contact reservoirs, mainly using strontium and oxygen isotopic geochemistry proxies combined with petrological and mineralogical analyses. Authigenic hydrothermal calcites are distinguished, and their distribution characteristics are examined in thermal contact rocks. Subsequently, the origin and migration trace of the hydrothermal-related calcites are clarified. This study has significant implications for intensive understanding of reservoir development mechanism and reservoir evaluation and prediction of thermal contact clastic rocks.

The State-of-the-Art Techniques of Hydrocarbon Detection and Its Application in Ultra-Deep Carbonate Reservoir Characterization in the Sichuan Basin, China

The Sichuan Basin is one of the most important gas-bearing basins in China, and its production accounts for more than a quarter of the country. In the past 10 years, major natural gas exploration discoveries in the basin were in ultra-deep ancient carbonate formations. The discovery of large marine gas fields has gone through a long period of exploration. For example, the Anyue gas field was discovered after more than 40 years of exploration. The main difficulty stems from deep burial, old age, and the complex geological evolution history of the carbonate rock and the resulting difficulty in identifying gas-bearing reservoirs. Although state-of-the-art reservoir prediction techniques have been used, the success rate of the exploration wells is relatively low. At present, the success rate of the ultra-deep exploration wells is about 30%. To enhance the reliability of the hydrocarbon detection of the ultra-deep carbonate reservoirs, we have developed a few novel methods in the last 10 years, including seismic-print analysis (SPA), depth-domain seismic dispersion analysis (DDSDA), and deep learning seismic analysis Applications to field data show that the results obtained by the new methods are in better agreement with the drilling results. This article presents the methods and their applications in the identification of the ultra-deep carbonate gas-bearing reservoirs in the Sichuan Basin, China. Key issues in hydrocarbon detection of ultra-deep carbonate reservoirs, which have not been solved well, are also discussed.

Elastic Impedance Simultaneous Inversion for Multiple Partial Angle Stack Seismic Data with Joint Sparse Constraint

Elastic impedance (EI) inversion for partial angle stack seismic data is a key technology in seismic reservoir prediction within the oil and gas industry. EI inversion provides a consistent framework to invert partial angle stack seismic data, just as the AI inversion does for post-stack data. The commonly used EI inversion process is angle by angle. Hence, the inverted EI for different angles may be nonconforming, especially for the seismic data with a low signal-to-noise ratio. This paper proposes to simultaneously invert multiple partial angle stack seismic data to obtain EI for different angles at once. To obtain conformable EI, we used the joint sparse constraint on the reflection coefficients for different angles. Then, the objective function for simultaneous EI inversion was constructed. Next, synthetic seismic data profiles with three different angles were used to show the superiority of the proposed EI inversion method compared to the conventional method. At last, a real seismic data line was used to test the feasibility of the proposed method in practice. The inversion results of synthetic data and real data showed that it provides an effective new alternative method to estimate EI from partial stack seismic data.

Study on Multi-Scale Coral Reef Pore Structure Identification and Characterization

In order to more accurately realize the automatic identification and quantitative characterization of coral reef multi-scale pore structure, this paper has carried out such research. On the basis of using multiple information acquisition technology, it can effectively collect field measured data and indoor observation data to form a high-precision digital image of the development characteristics of multi-scale coral reef pore structure. By constructing the multi-scale structure information correlation and fusion function of coral reef pore structure, it can formulate the positioning and search strategy for the key areas of coral reef pore structure development characteristics. In this paper, a fine identification and quantitative characterization method suitable for the pore structure of “millimeter–micron–nanometer” is formed. At the same time, combined with the actual project requirements, the multi-scale pore structure feature area location method of coral reef is constructed, which combines the distribution characteristics, variation characteristics, fractal characteristics and shape characteristics in a certain range, and realizes the search of pore key areas between different scales and different regions of the same scale through the similarity matching algorithm, which can effectively realize the automatic search of key areas in the “millimeter–micron–nanometer”-scale image of coral reefs. Finally, the correctness and feasibility of this method are verified by multi-angle example data comparison. It shows that this method can effectively solve the productivity prediction and evaluation problem of coral reef oil and gas reservoirs.

Improved fixed-point seismic inversion constrained by instantaneous phase

Some subtle reservoirs are hidden in the weak reflection areas and thin layers in seismic profiles, and they cannot be identified easily. Thus, the thin layer interpretation and the subtle reservoir prediction are the essential in reservoir management plan while field development and production stage. To improve the recognition capability in the weak reflection areas and resolution, the fixed-point (FP) algorithm is employed to obtain the inversion results since the conventional FP seismic inversion (CFPSI) is sensitive to the weak changes in seismic signals. However, the CFPSI suffers from low convergence. Therefore, the new iterative equations of pre- and post-stack FP seismic inversion are designed to improve the computational efficiency and convergence. Besides, a new constraint, which is derived from the instantaneous phase of the seismic trace, is also sensitive to the weak changes of the seismic signal. The instantaneous phase constraint is then involved in the objective function, contributing to a significant improvement of identification capability in weak reflection areas and the resolution of inversion. Additionally, the initial model constraint is introduced into the objective function to weaken the multi-solution and noise sensitivity. The synthetic examples demonstrate that the computational efficiency, recognition capability in the weak reflection areas and resolution of the proposed approach are higher than those of the CFPSI. The field examples suggest that the subtle reservoirs and thin layers, which are hidden in the weak reflection areas in the field data, are accurately identified using our method. Thus, the proposed method is useful for improving the recognition capability in weak reflection areas and resolution of the inversion results.

Multi-strategy Slime Mould Algorithm for hydropower multi-reservoir systems optimization

The challenge to determine the best policies for hydropower multiple reservoir systems is a high-dimensional and nonlinear problem, making it challenging to attain a global solution. To efficiently optimize such a complicated solution, the creation of a high-precision optimization algorithm is critical. Hence, this research proposes a Multi-strategy Slime Mould Algorithm (MSMA) to determine the optimal operating rules for a complicated hydropower multiple reservoir prediction problem. The MSMA system proposed employs an effective wrap food mechanism to strengthen local and global capability; an enhanced solution quality (ESQ) to promote solution quality; and the interior-point method to implement an influential exploitation mechanism. The numerical testing of 23 test functions demonstrates the efficiency of the MSMA algorithm in solving global optimization issues. The newly developed method is then used to optimize the operation of a complex eight-reservoir hydropower system, with the proposed MSMA approach resulting in ∼0.999% of an ideal global solution, according to the optimal findings. The results of the multi-reservoir system show that proposed MSMA method was able to generate about 16.6% more power than the SMA. Consequently, the recommended method outperforms the other well-known optimization methods for maximizing power in the multi-reservoir system. Finally, this study also provides a useful tool for optimizing the complicated hydropower multiple reservoir problems.

Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs

Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. The performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model.

Study on the Microscopic Pore Structures of Coal Measure Reservoirs in the Shanxi Formation, Eastern Ordos Basin

The Carboniferous-Permian coal measures in China contain abundant natural gas resources. Shale, coal and tight sandstone reservoirs are developed in coal measures, and the quantitative characterization of the pore structures of different types of reservoirs can provide scientific guidance for the sweet spot prediction of tight reservoirs. In this study, taking the Shan 2 Member coal measure of the Shanxi Formation in the eastern Ordos Basin as an example, the pore structures of shale, coal rock and tight sandstone were systematically studied based on organic geochemistry, scanning electron microscopy, high-pressure mercury injection, and low-temperature N2 and CO2 adsorption experiments. The results show that the microscopic pore structures of different types of reservoirs in the Shan 2 Member coal measures are quite different. Shale and tight sandstone mainly develop clay mineral pores at mesopore scale, followed by intragranular and dissolution pores developed in quartz and feldspar minerals, while organic pores are rarely developed. A large number of macro-scale clay mineral pores and micro-fractures are developed in tight sandstone, meanwhile the pore connectivity of tight sandstone is better than that of shale. A large number of micro to nano-scale organic pores are developed in coal, and the specific surface area of micropores in coal is much larger than that of mesopores in shale and tight sandstone. Sandstone, shale, and coal are frequently interbedded in coal measure strata. Tight sandstone provide the main storage space for free gas, and pores in shale and coal absorb a large amount of natural gas. Sandstone-shale-coal assemblages and sandstone-coal assemblages are the key targets for the exploration of hydrocarbons in the Shanxi Formation coal measures in the study area.

Lithology Prediction of One-dimensional Residual Network Based on Regularization Constraints

Lithology prediction is an important work in seismic reservoir prediction. Deep learning can explore the nonlinear mapping relationship between lithology and seismic properties, and achieve efficient and accurate lithology prediction. On the one hand, when the depth of the network increases, the problem of model degradation is prone to occur. On the other hand, due to the small sample size of logging data, overfitting is common when deep learning methods are used for lithology prediction. We apply a one-dimensional residual network to lithology prediction with regularization constraints on the overfitting phenomenon of the model. According to the change of loss function under different regularization constraint methods, the influence of regularization constraints on model overfitting is analyzed. Compared with the initial model, the prediction accuracy of the model with regularization constraints in the validation set is improved from 48.81% to 59.87%. When considering adjacent lithology, the validation set accuracy improves from 89.37% to 91.54%. The proposed model achieves 92.65% accuracy on the test set. Applying a regularized residual network model to seismic data prediction can effectively indicate the distribution of subsurface lithology.

Geophysical investigation of hydrocarbon in the southern part of Douala Kribi Campo basin, Cameroon

Well logging analysis and 3D seismic method are combined to evaluate the nature of fluids and reservoir characteristics in the southern part of the Douala/Kribi-Campo basin (DKC). Scientifiques researches that exhibit the important geological and geophysical settings of this basin are lacked. The successful investigation of these areas is possible if the stratigraphical and petrophysical properties of reservoir are properly evaluated. Extracting facies types from 3D seismic sections is a rapidly evolving discipline that facilitates the development of reservoir prediction models. So, a 25m × 25m seismic grid associated with Gamma-Ray log (GR), Neutron (Nphi) log, resistivity log, sonic log and density (RHOB) log have been performed. The results highlight the sedimentary environment and facies, porous-permeable clay banks, clay content, the porosity, saturation and the nature of the fluids in the reservoir. The petrophysical evaluations indicates the presence of potential petroleum reservoirs, mainly sandy-clay with 3.81–29.47% volume, 17.37–27.85% porosity and 47.74–56.02% water saturation. The nature of fluids present in the reservoir is oil, gas and water. This research work will serve to encourage hydrocarbon (HC) exploration in the DKC Basin by adopting strategies where seismic stratigraphy combined with well datas will be the most likely means of providing drill targets to more independent operators. The results of this study will serve for the future prospector on the neighboring oil and gas fields of our study area. Significant advances has been made to ensure future exploration success.

Reservoir production prediction with optimized artificial neural network and time series approaches

Numerical simulation of oil reservoirs is one of the most commonly used methods for reservoir production prediction, but its accuracy is based on accurate geological modeling and high-quality history matching. Therefore, numerical simulation is time-consuming and costly and requires extensive information. Traditional back-propagation neural networks and their improved algorithms are widely used for production prediction, but they are not suitable for time-series prediction problems. Based on variations in oil production, this study proposes a reservoir production prediction model based on a combined convolutional neural network (CNN) and a long short-term memory (LSTM) neural network model optimized by the particle swarm optimization (PSO) algorithm. First, the model extracts important temporal data features through the upper CNN, which is next imported to the lower LSTM network to further extract correlation features in the time dimension; then, it iteratively optimizes the key hyperparameters in the CNN-LSTM model through the PSO algorithm; finally, it uses the trained model for reservoir prediction. Compared with the training results of the LSTM neural network and CNN model, the PSO–CNN-LSTM model has higher prediction accuracy in time-series production prediction. Our proposed hybrid model is a data-driven method and is based on routinely available production data. Quick and accurate production prediction can lead to better informed operational decisions and optimization of recovery and economics.