Carbonate Reservoir Prediction Research Articles

Reservoir identification of marine facies carbonate shoals in the Middle Permian in the Sichuan Basin

Controlled by the base level change cycle, the carbonates exposed from the periodically shallowed water body at the top of the high-frequency sequence develops multiple sets of thin reservoirs. Such reservoir identification has always been tricky in the prediction of carbonate reservoirs. Based on the Middle Permian strata from the Sichuan Basin, this paper established a forecasting approach of high-frequency-sequence-controlled carbonate thin reservoir. The steps are as follows: (1) Combine the measured results of samples from the typical outcrops with changes of carbon and oxygen isotopes, thus to construct a division program for the 3rd-order sequence of the Middle Permian strata. (2) Compare the carbon and oxygen isotope changes and the global sea level change trend, determine the controlling factors of the 3rd-order sequences. (3) Carry out spectrum analysis based on the natural gamma energy spectrum logging (ln (Th/K)) curves of Wells Long-17 and Anping-1 to establish a high-frequency sequence framework division program, and then apply the logging curve characteristics to correct the top interface positions of the high-frequency sequences, which is the potential location of carbonate reservoir. The research results suggest that the Middle Permian in the Sichuan Basin can be divided into three 3rd-order sequences, which are PSQ1, PSQ2 and PSQ3 from bottom to top. Among which, PSQ1 is mainly affected by global sea level (GSL) changes, and the changes originated from its tuning curve can be directly applied to the division of high-frequency sequences and reservoir prediction. PSQ2 and PSQ3 are jointly controlled by global sea level changes and regional tectonics. In cope with the influence of regional tectonics, the positions of the four-level sequence interface established according to the tuning curve need to be fully considered, and good results were achieved through systematical correction. This method can effectively reduce the risk of reservoir prediction caused by marine carbonate heterogeneity, and provide scientific foundation for the prediction of high-frequency cyclic reservoirs of platform facies and platform margin subfacies in other marine carbonate sedimentary basins.

Comprehensive pore structure characterization and permeability prediction of carbonate reservoirs using high-pressure mercury intrusion and X-ray CT

Comprehensive pore structure characterization and permeability prediction of carbonate reservoirs using high-pressure mercury intrusion and X-ray CT

An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes

This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes.

Theoretical and technical progress in exploration practice of the deep-water large oil fields, Santos Basin, Brazil

The history and results of petroleum exploration in the Santos Basin, Brazil are reviewed. The regularity of hydrocarbon enrichment and the key exploration technologies are summarized and analyzed using the seismic, gravity, magnetic and drilling data. It is proposed that the Santos Basin had a structural pattern of two uplifts and three depressions and the Aram-Uirapuru uplift belt controlled the hydrocarbon accumulation. It is believed that the main hydrocarbon source kitchen in the rift period controlled the hydrocarbon-enriched zones, paleo-structures controlled the scale and quality of lacustrine carbonate reservoirs, and continuous thick salt rocks controlled the hydrocarbon formation and preservation. The process and mechanism of reservoirs being transformed by CO2 charging were revealed. Five key exploration technologies were developed, including the variable-velocity mapping for layer-controlled facies-controlled pre-salt structures, the prediction of lacustrine carbonate reservoirs, the prediction of intrusive/effusive rock distribution, the detection of hydrocarbons in lacustrine carbonates, and the logging identification of supercritical CO2 fluid. These theoretical recognitions and exploration technologies have contributed to the discovery of deep-water super-large reservoirs under CNODC projects in Brazil, and will guide the further exploration of deep-water large reservoirs in the Santos Basin and other similar regions.

Application of Seismic Waveform Indicator Inversion in the Depth Domain: A Case Study of Pre-Salt Thin Carbonate Reservoir Prediction

Prestack depth-migrated seismic data, having more accurate imaging position and amplitude fidelity than prestack time-migrated seismic data, are supposed to produce a higher quality reservoir prediction result by using depth-domain inversion. Some researchers have developed different methods of depth-domain seismic inversion. However, it has not been widely used in the industry probably because of two reasons: (1) it is a complex process to conduct depth-domain seismic inversion due to the nonstationary depth-domain seismic wavelet; and (2) time-domain seismic inversion is considered capable of solving the problem with less cost, both in regard to time and the economy. In this paper, we try to use the seismic waveform indicator inversion method in the depth domain. First, a forward model was built to demonstrate that seismic waveforms both in the time domain and the depth domain are highly correlated with lithologic associations. Second, a quantitative evaluation method of seismic data for reservoir prediction was proposed, which can help geophysicists estimate time-domain and depth-domain inversion effects before inversion. Finally, the seismic waveform indicator inversion method was implemented for presalt thin carbonate reservoir prediction in the Central Block at the eastern margin of the Pre-Caspian Basin. The depth-domain inversion result shows a relatively true structure and higher resolution validated by wells.

Hilbert–Huang Transform Incorporating Analytical Mode Decomposition and Its Application in Fractured-Vuggy Carbonate Reservoir Prediction

High-precision time-frequency (TF) analysis (TFA) can accurately detect anomalies in oil and gas reservoirs. However, conventional TFA methods cannot effectively identify fractured-vuggy carbonate reservoirs, which are deep and strongly heterogeneous. In this paper, a TFA method based on analytical mode decomposition (AMD) and the Hilbert-Huang transform (HHT) is proposed. Specifically, seismic signals are decomposed by empirical mode decomposition (EMD) and then by AMD. Finally, the TF distribution of the seismic signals is obtained after Hilbert spectrum analysis (HSA). Synthetic seismic data demonstrate that the AMD-HHT method can effectively eliminate mode mixing and provide higher time and frequency resolutions and energy focus than conventional TFA methods. In addition, model and field seismic data also verify that the AMD-HHT method can effectively detect hydrocarbon-related energy anomalies hidden in broadband seismic data.

Quantitative characterization of deep fault-karst carbonate reservoirs: A case study of the Yuejin block in the Tahe oilfield

The Ordovician reservoirs in the Tahe oilfield are dominated by fractured-vuggy carbonate reservoirs, of which fault-karst reservoirs are a hot topic in recent years. Fault-karst reservoirs feature high production, large burial depth, and strong heterogeneity under the control of faulting and karstification. Based on geological, logging, and seismic data, this study classified the Ordovician fault-karst reservoirs in the Yuejin block of the Tahe oilfield into three types, namely karst-cave, dissolved-vug, and fractured types, and established the integrated identification criteria of the three types of reservoirs. This study characterized karst caves, dissolved vugs, and multi-scale faults through seismic wave impedance inversion and frequency-domain detection of multi-scale faults. 3D geological models of different types of reservoirs were built using the combined deterministic and stochastic methods and characterized the spatial distribution of multi-scale faults, karst caves, dissolved vugs, and physical property parameters of reservoir. This study established the method for the geological modeling of fault-karst reservoirs, achieved the quantitative characterization and revealed the heterogeneity of fault-karst reservoirs. The karst-cave and dissolved-vug types are high in porosity and act as reservoirs, while the fractured type is high in permeability and act as flow pathway. This study lays the foundation for the development index prediction, well emplacement, and efficient development of the fault-karst carbonate reservoirs.

Deep carbonate reservoir and gas prediction based on multicomponent seismic amplitude attributes — A case study

Multicomponent seismic exploration provides more valuable information for the prediction of underground structures, lithology, fluids, and fractures. Most studies on lithology estimation and fluid description using PP- and PS-wave seismic data mainly focus on relatively shallow targets with high porosity. Focusing on deep carbonate (dolomite) reservoirs with low porosity (generally less than 5%), we first analyze the characteristics of the seismic response difference between PP and PS waves, and then we combine logging data and seismic forward modeling to implement the identification of fluids using the amplitude difference between PP and PS waves. Data sets are acquired over grain beach dolomite reservoirs in the Cambrian Longwangmiao Formation, the central Sichuan Basin, Western China. The reservoirs are filled with different types of fluids (e.g., gas, water, bitumen, etc.). Based on comparisons between seismic responses of PP and PS waves, we observe that, at the location of the gas-bearing layer, PP-wave amplitude exhibits strong peaks and PS-wave amplitude finds relatively weak peaks, which is different from that obtained for the water-saturated layer. However, at the location of bitumen-filled reservoirs, PP- and PS-wave amplitudes exhibit strong peaks, and the reflection amplitude of PP waves is stronger than that of PS waves. Therefore, the amplitude difference of PP and PS waves provides valuable information and feasibility for fluid identification. We verify that the maximum peak amplitude attribute of PS waves may better characterize the distribution of porous dolomite reservoirs than that of PP waves, and using the attribute of maximum peak amplitude difference between PP and PS waves, we may distinguish gas-bearing and water-saturated layers. Comparing with actual drilling results, we conclude that our PP/PS interpretation technique is feasible for fluid identification in deep carbonate reservoirs.

A Carbonate Reservoir Prediction Method Based on Deep Learning and Multiparameter Joint Inversion

Deep-water carbonate reservoirs are currently the focus of global oil and gas production activities. The characterization of strongly heterogeneous carbonate reservoirs, especially the prediction of fluids in deep-water presalt carbonate reservoirs, exposes difficulties in reservoir inversion due to their complex structures and weak seismic signals. Therefore, a multiparameter joint inversion method is proposed to comprehensively utilize the information of different seismic angle gathers and the simultaneous inversion of multiple seismic parameters. Compared with the commonly used simultaneous constrained sparse-pulse inversion method, the multiparameter joint inversion method can characterize thinner layers that are consistent with data and can obtain higher-resolution presalt reservoir results. Based on the results of multiparameter joint inversion, in this paper, we further integrate the long short-term memory network algorithm to predict the porosity of presalt reef reservoirs. Compared with a fully connected neural network based on the backpropagation algorithm, the porosity results are in better agreement with the new log porosity curves, with the average porosity of the four wells increasing from 89.48% to 97.76%. The results show that the method, which is based on deep learning and multiparameter joint inversion, can more accurately identify porosity and has good application prospects in the prediction of carbonate reservoirs with complex structures.

Deep carbonate reservoir characterisation using multi-seismic attributes via machine learning with physical constraints

AbstractSeismic characterisation of deep carbonate reservoirs is of considerable interest for reservoir distribution prediction, reservoir quality evaluation and reservoir structure delineation. However, it is challenging to use the traditional methodology to predict a deep-buried carbonate reservoir because of the highly nonlinear mapping relationship between heterogeneous reservoir features and seismic responses. We propose a machine-learning-based method (random forest) with physical constraints to enhance deep carbonate reservoir prediction performance from multi-seismic attributes. We demonstrate the effectiveness of this method on a real data application in the deep carbonate reservoir of Tarim Basin, Western China. We first perform feature selection on multi-seismic attributes, then four kinds of physical constraint (continuity, boundary, spatial and category constraint) transferred from domain knowledge are imposed on the process of model building. Using the physical constraints, the F1 score of reservoir quality and reservoir type can be significantly improved and the combination of the effective physical constraints gives the best prediction of performance. We also apply the proposed strategy on 2D seismic data to predict the spatial distribution of reservoir quality and type. The seismic prediction results provide a reasonable description of the strong heterogeneity of the reservoir, offering insights into sweet spot detection and reservoir development.

Mathematical modelling of carbonate reservoir dissolution and prediction of the controlled hydrochloric acid treatment efficiency

The article presents the theoretical studies results of hydrochloric acid compositions filtration in carbonate collectors porous media saturated with two-phase formation liquid. Solution of filtration problem in the process of carbonate rock leaching with possible regulation of process by hydrocarbon solvents is considered. Numerical algorithm of acid effect on oil-saturated formation is proposed and tested, which allows to determine the following parameters of filtration flow: concentration of hydrochloric acid, distribution of water saturation, pressure and other parameters. A mathematical model of the carbonate collector dissolution process using composite solvents has been developed, which allows predicting technological indicators of acid impact efficiency.

Depositional facies and diagenesis of the Lianglitage Formation in northwestern Tazhong Uplift, Tarim Basin, China: implications for the genesis of ultra-deep limestone reservoir

The limestone reservoir beds of Late Ordovician Lianglitage Formation are preserved in northwestern Tazhong Uplift of the Tarim Basin with buried depth exceeding 6000 m. The origin of the ultra-deep limestone reservoir is still controversial, which restricts the further hydrocarbon exploration. In order to clarify the main controlling factors of reservoirs, we carried out the study of sedimentary facies, diagenesis, and reservoir characteristics based on seismic, well logging, and core data. The Lianglitage Formation in northwestern Tazhong Uplift is mainly composed of nine lithofacies that are deposited from platform margin reef-shoal, platform-interior shoal, and restricted lagoon to tidal flat environments. The Lianglitage Formation has experienced syndepositional submarine diagenesis, penecontemporaneous diagenesis, and burial diagenesis, and consequently the reservoir space in the formation mainly includes fractures and dissolution vugs and caves. Reef and shoal deposits are the material basis of favorable reservoirs, and the favorable reservoirs have been mostly improved by the dissolution of meteoric water in penecontemporaneous diagenesis. In the burial stage, the early-formed porosity was destroyed by cementation to some extent, but the fractures and associated vugs formed in the late Ordovician and Silurian-Devonian played an important role in the improvement of the reservoir. The results are of great significance to the prediction of deep carbonate reservoirs and the hydrocarbon exploration in basins of western China. Favorable facies, early-stage improvement by meteoric water, and tectonic fracture are key to the formation of high-quality large-scale deep limestone reservoirs.

Research and application of seismic porosity inversion method for carbonate reservoir based on Gassmann’s equation

Abstract Carbonate rocks frequently exhibit less predictable seismic attribute–porosity relationships because of complex and heterogeneous pore geometry. Pore geometry plays an important role in carbonate reservoir interpretation, as it influences acoustic and elastic characters. So in porosity prediction of carbonate reservoirs, pore geometry should be considered as a factor. Thus, based on Gassmann’s equation and Eshelby–Walsh ellipsoidal inclusion theory, we introduced a parameter C to stand by pore geometry and then deduced a porosity calculating expression from compressional expression of Gassmann’s equation. In this article, we present a porosity working flow as well as calculate methods of every parameter needed in the porosity inverting equation. From well testing and field application, it proves that the high-accuracy method is suitable for carbonate reservoirs.

Prediction of Favorable Carbonate Reservoirs under Extremely Thick Salts via Poststack Facies-Controlled and Prestack Zoeppritz Equation Inversions in the Santos Basin of Brazil

Subsalt carbonate rocks in Brazil’s deepwater region have broad prospects for oil and gas exploration and development. Due to the low-frequency bandwidth of the seismic data and the poor signal quality for this kind of reservoir target, there is a demand for accurate seismic prediction methods. We employ the facies-controlled inversion using a low-pass filter matrix to ensure the accuracy of the low frequency and to improve the robustness of the inversion results. We integrated the concept of adaptive regularization constraint of the Zoeppritz equation into the generalized linear inversion theory framework, which overcomes the shortcomings of the approximate equation. Making full use of the large angle prestack seismic information, Zoeppritz equation inversion improves the accuracy of the inversion results. The application of this method in carbonate reservoirs under extremely thick salts in the Santos Basin of Brazil indicates the feasibility and practicality of the proposed integrated prediction method.

Application of VSP well drive data in the prediction of complex carbonate reservoirs

The qixia formation carbonate rocks in the northwest of sichuan are of great buried depth, thin reservoir thickness and strong heterogeneity. Therefore, VSP data are used to control the seismic data on the surface, VSP well drive data lay a good foundation for interpretation. Then, through waveform clustering analysis, favorable facies zones were developed. Secondly, natural faults are predicted based on maximum likelihood attribute technique. Finally, the reservoir quantitative prediction is carried out by using phase control inversion technique. The drilling well location is optimized based on reservoir and fracture prediction information, and the drilling effect is good, which provides strong technical support for subsequent exploration and development in this area.

New Method for Predicting Production Boosts Accuracy for Carbonate Reservoirs

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 190868, “A New Method for Economic Prediction of Carbonate Reservoirs Based on Expert Library and Small Database,” by Wenbin Chen, SPE, Hanqiao Jiang, and Junjian Li, China University of Petroleum; Shan Jiang, PetroChina; and Hanxu Yang, SPE, and Yan Qiao, China University of Petroleum, prepared for the 2018 EAGE Annual Conference and Exhibition/SPE Europec, Copenhagen, Denmark, 11–14 June. The paper has not been peer reviewed. The widely developed karst caves and fractures in carbonate reservoirs result in strong spatial heterogeneity. Consequently, the parameters obtained from cores and numerical simulation are limited in their ability to reflect the production possibility of the entire reservoir. To solve this problem, this paper proposes a new method of economic prediction on the basis of expert library and oilfield databases. The method takes into account geological factors and the effect of production factors on the economic prediction. Introduction The key parameters used for the economic prediction of a carbonate reservoir include well spacing, oil production, and annual decline rate. These parameters are mainly from core experiments in the laboratory or from the numerical simulation of wells, but caves and fractures are dispersed throughout the carbonate reservoir. This heterogeneity means the properties of a carbonate reservoir can be very different in different regions. Consequently, the commonly used parameters are limited in their ability to represent the whole reservoir, which leads to high risk in economic prediction. The method proposed in this paper combines the Delphi method, the analytic hierarchy process (AHP), the technique for order preference by similarity to ideal solution (TOPSIS), the moving least-squares (MLS) method, and future net present value (FNPV) to create the DATMF method in an attempt to overcome these shortcomings. Through classical mathematics methods, the DATMF method combines expert library and oilfield databases to predict the economy of carbonate reservoirs. This prediction method reduces the size of the required database greatly, and its calculation process is based on professional geology theory and reservoir engineering. In addition, the DATMF economic prediction method can be applied directly to the stage of geological exploration, which removes the barrier between geologists and reservoir engineers and simplifies the procedure of economic prediction. DATMF Work Flow Delphi Method. The Delphi method is a management strategy for dealing with complex task problems. Here, it aims to quantify experience and ensure the reliability of results in theory. After experts learn the purpose and background of research, they compare the effect of factors of the same level. Two factors are compared each time. After repeated inquiry and feedback, the experts finally reach an agreement of opinion and a final expert judgment matrix is established and expressed as Matrix A. Because of the independent judgment environment, the objectivity of the Delphi method is strong and reliable.

Characteristics and favorable area prediction of Ordovician buried-hill carbonate reservoirs in the Bozhong 21–2 tectonic belt, Bohai Bay Basin, China

ABSTRACTBased on core data, thin section and logging, this study investigates the basic characteristics and occurrence regularities of the Ordovician buried-hill carbonate reservoir in the Bozhong 21-2 tectonic belt. Results show that the reservoir lithology is primarily limestone, followed by dolomite, dolomitic limestone or limey dolomite, and silty mudstone. The reservoir spaces are primarily composed of secondary porosity. These reservoirs can be divided vertically into weathered-crust karst reservoirs and inner dissolution-type reservoirs. The distribution of reservoirs is controlled chiefly by karstification. The high karst units in the study area are favorable for the development of the two reservoir types.

Seismic response and prediction of Ordovician fractured vuggy carbonate reservoirs in the Tazhong Shunnan well A block

Ordovician fractured vuggy carbonate reservoirs, which are deeply buried in the Tazhong Shunnan area in China, are characterized by high heterogeneity. Meanwhile, there is no significant difference between the geophysical characteristics of the reservoirs and that of the surrounding rocks. We have introduced the multiscale stack random medium theory and built some theoretical seismic-geologic models for the fractured vuggy carbonate reservoirs. Furthermore, we obtained the seismic reflection characteristics corresponding to these models using finite-difference forward modeling. The small random vugs are characterized by weak and chaotic reflections with high frequency, and the large vugs are characterized by strong and chaotic reflections with low frequency. The amplitude of the seismic reflections increases with the increasing vug density, and it decreases with the increasing roughness factor. Combining the synthetic reflection characteristics corresponding to the fractured vuggy carbonate reservoirs and the actual seismic reflections from the drilled reservoirs, we summarized the recognition patterns of the carbonate reservoirs. The predicted results found that the potential fractured vuggy reservoirs at the top of Yijianfang Formation are located in the southwest and northeast, in the vicinity of fault zones. The reservoirs in Peng-Laiba Formation are distributed in the northwest of the block.

Experiment of Carbonate Dissolution: Implication for High Quality Carbonate Reservoir Formation in Deep and Ultradeep Basins

As the most frontiers in petroleum geology, the study of dissolution-based rock formation in deep carbonate reservoirs provides insight into pore development mechanism of petroleum reservoir space, while predicting reservoir distribution in deep-ultradeep layers. In this study, we conducted dissolution-precipitation experiments simulating surface to deep burial environments (open and semiopen systems). The effects of temperature, pressure, and dissolved ions on carbonate dissolution-precipitation were investigated under high temperature and pressure (~200°C; ~70 Mpa) with a series of petrographic and geochemical analytical methods. The results showed that the window-shape dissolution curve appeared in 75~150°C in the open system and 120~175°C in the semiopen system. Furthermore, the dissolution weight loss of carbonate rocks in the open system was higher than that of semiopen system, making it more favorable for gaining porosity. The type of fluid and rock largely determines the reservoir quality. In the open system, the dissolution weight loss of calcite was higher than that of dolomite with 0.3% CO2as the reaction fluid. In the semiopen system, the weight loss from dolomitic limestone prevailed with 0.3% CO2as the reaction fluid. Our study could provide theoretical basis for the prediction of high quality carbonate reservoirs in deep and ultradeep layers.

The identification of multi-cave combinations in carbonate reservoirs based on sparsity constraint inverse spectral decomposition

Sparsity constraint inverse spectral decomposition (SCISD) is a time-frequency analysis method based on the convolution model, in which minimizing the l1 norm of the time-frequency spectrum of the seismic signal is adopted as a sparsity constraint term. The SCISD method has higher time-frequency resolution and more concentrated time-frequency distribution than the conventional spectral decomposition methods, such as short-time Fourier transformation (STFT), continuous-wavelet transform (CWT) and S-transform. Due to these good features, the SCISD method has gradually been used in low-frequency anomaly detection, horizon identification and random noise reduction for sandstone and shale reservoirs. However, it has not yet been used in carbonate reservoir prediction. The carbonate fractured-vuggy reservoir is the major hydrocarbon reservoir in the Halahatang area of the Tarim Basin, north-west China. If reasonable predictions for the type of multi-cave combinations are not made, it may lead to an incorrect explanation for seismic responses of the multi-cave combinations. Furthermore, it will result in large errors in reserves estimation of the carbonate reservoir. In this paper, the energy and phase spectra of the SCISD are applied to identify the multi-cave combinations in carbonate reservoirs. The examples of physical model data and real seismic data illustrate that the SCISD method can detect the combination types and the number of caves of multi-cave combinations and can provide a favourable basis for the subsequent reservoir prediction and quantitative estimation of the cave-type carbonate reservoir volume.