SPE Reservoir Evaluation & Engineering
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Published By Society Of Petroleum Engineers

1094-6470

2021 ◽  
pp. 1-17
Author(s):  
Enda Du ◽  
Yuetian Liu ◽  
Ziyan Cheng ◽  
Liang Xue ◽  
Jing Ma ◽  
...  

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.


2021 ◽  
pp. 1-18
Author(s):  
Shaoqing Sun ◽  
David A. Pollitt

Summary Benchmarking the recovery factor and production performance of a given reservoir against applicable analogs is a key step in field development optimization and a prerequisite in understanding the necessary actions required to improve hydrocarbon recovery. Existing benchmarking methods are principally structured to solve specific problems in individual situations and, consequently, are difficult to apply widely and consistently. This study presents an alternative empirical analog benchmarking workflow that is based upon systematic analysis of more than 1,600 reservoirs from around the world. This workflow is designed for effective, practical, and repeatable application of analog analysis to all reservoir types, development scenarios, and production challenges. It comprises five key steps: (1) definition of problems and objectives; (2) parameterization of the target reservoir; (3) quantification of resource potential; (4) assessment of production performance; and (5) identification of best practices and lessons learned. Problems of differing nature and for different objectives require different sets of analogs. This workflow advocates starting with a broad set of parameters to find a wide range of analogs for quantification of resource potential, followed by a narrowly defined set of parameters to find relevant analogs for assessment of production performance. During subsequent analysis of the chosen analogs, the focus is on isolating specific critical issues and identifying a smaller number of applicable analogs that more closely match the target reservoir with the aim to document both best practices and lessons learned. This workflow aims to inform decisions by identifying the best-in-class performers and examining in detail what differentiates them. It has been successfully applied to improve hydrocarbon recovery for carbonate, clastic, and basement reservoirs globally. The case studies provided herein demonstrate that this workflow has real-world utility in the identification of upside recovery potential and specific actions that can be taken to optimize production and recovery.


2021 ◽  
pp. 1-18
Author(s):  
Andres Gonzalez ◽  
Zoya Heidari ◽  
Olivier Lopez

Summary Core measurements are used for rock classification and improved formation evaluation in both cored and noncored wells. However, the acquisition of such measurements is time-consuming, delaying rock classification efforts for weeks or months after core retrieval. On the other hand, well-log-based rock classification fails to account for rapid spatial variation of rock fabric encountered in heterogeneous and anisotropic formations due to the vertical resolution of conventional well logs. Interpretation of computed tomography (CT) scan data has been identified as an attractive and high-resolution alternative for enhancing rock texture detection, classification, and formation evaluation. Acquisition of CT scan data is accomplished shortly after core retrieval, providing high-resolution data for use in petrophysical workflows in relatively short periods of time. Typically, CT scan data are used as two-dimensional (2D) cross-sectional images, which is not suitable for quantification of three-dimensional (3D) rock fabric variation, which can increase the uncertainty in rock classification using image-based rock-fabric-related features. The methods documented in this paper aim to quantify rock-fabric-related features from whole-core 3D CT scan image stacks and slabbed whole-core photos using image analysis techniques. These quantitative features are integrated with conventional well logs and routine core analysis (RCA) data for fast and accurate detection of petrophysical rock classes. The detected rock classes are then used for improved formation evaluation. To achieve the objectives, we conducted a conventional formation evaluation. Then, we developed a workflow for preprocessing of whole-core 3D CT-scan image stacks and slabbed whole-core photos. Subsequently, we used image analysis techniques and tailor-made algorithms for the extraction of image-based rock-fabric-related features. Then, we used the image-based rock-fabric-related features for image-based rock classification. We used the detected rock classes for the development of class-based rock physics models to improve permeability estimates. Finally, we compared the detected image-based rock classes against other rock classification techniques and against image-based rock classes derived using 2D CT scan images. We applied the proposed workflow to a data set from a siliciclastic sequence with rapid spatial variations in rock fabric and pore structure. We compared the results against expert-derived lithofacies, conventional rock classification techniques, and rock classes derived using 2D CT scan images. The use of whole-core 3D CT scan image-stacks-based rock-fabric-related features accurately captured changes in the rock properties within the evaluated depth interval. Image-based rock classes derived by integration of whole-core 3D CT scan image-stacks-based and slabbed whole-core photos-based rock-fabric-related features agreed with expert-derived lithofacies. Furthermore, the use of the image-based rock classes in the formation evaluation of the evaluated depth intervals improved estimates of petrophysical properties such as permeability compared to conventional formation-based permeability estimates. A unique contribution of the proposed workflow compared to the previously documented rock classification methods is the derivation of quantitative features from whole-core 3D CT scan image stacks, which are conventionally used qualitatively. Furthermore, image-based rock-fabric-related features extracted from whole-core 3D CT scan image stacks can be used as a tool for quick assessment of recovered whole core for tasks such as locating best zones for extraction of core plugs for core analysis and flagging depth intervals showing abnormal well-log responses.


2021 ◽  
pp. 1-18
Author(s):  
Takaaki Uetani ◽  
Hiromi Kaido ◽  
Hideharu Yonebayashi

Summary Low-salinity water (LSW) flooding is an attractive enhanced oil recovery (EOR) option, but its mechanism leading to EOR is poorly understood, especially in carbonate rock. In this paper, we investigate the main reason behind two tertiary LSW coreflood tests that failed to demonstrate promising EOR response in reservoir carbonate rock; additional oil recovery factors by the LSW injection were only +2% and +4% oil initially in place. We suspected either the oil composition (lack of acid content) or the recovery mode (tertiary mode) was inappropriate. Therefore, we repeated the experiments using an acid-enriched oil sample and injected LSW in the secondary mode. The result showed that the low-salinity effect was substantially enhanced; the additional oil recovery factor by the tertiary LSW injection jumped to +23%. Moreover, it was also found that the secondary LSW injection was more efficient than the tertiary LSW injection, especially in the acid-enriched oil reservoir. In summary, it was concluded that the total acid number (TAN) and the recovery mode appear to be the key successful factors for LSW in our carbonate system. To support the conclusion, we also performed contact angle measurement and spontaneous imbibition tests to investigate the influence of acid enrichment on wettability, and moreover, LSW injection on wettability alteration.


2021 ◽  
pp. 1-14
Author(s):  
Elizaveta Shvalyuk ◽  
Alexei Tchistiakov ◽  
Alexandr Kalugin

Summary The main objective of this study was to provide rock typing of the producing formation based on high-resolution computed tomography (CT) scanning and nuclear magnetic resonance (NMR) data in combination with routine core analyses results. The target formation is composed of a shallowing up sequence of clastic rocks. Siltstones in its base are gradually replaced by sandstones toward its top. Initially, only sandstones were considered as oil-bearing, while siltstones were considered as water-bearing based on saturation calculation by means of Archie’s equation (Archie 1942) with the same values of cementation and saturation exponent for the whole formation. However, follow-up well tests detected considerable oil inflow also from the base of the reservoir composed of siltstones. Therefore, better rock typing was needed to improve the initial saturation distribution calculation. An applied approach that was based on integrated analysis of rock microstructural characteristics and derived from the NMR and CT techniques and conventional properties used for reserves calculation appeared to be an effective tool for rock typing polymineral clastic reservoirs. Measuring porous network characteristics and conventional properties in the same core plug enables a confident correlation between all measured parameters. Consequently, rock typing of samples based on flow units’ microstructural characteristics derived from NMR and CT scanning has shown a very good consistency with each other. As a result, four rock types were distinguished within a formation, which were previously interpreted as a single rock type. The detailed rock typing of the reservoir allowed more accurate reserves calculation and involvement of additional intervals into the production. Besides porous media characterization, CT scanning proved to be an effective tool for detecting minerals, such as pyrite and carbonates, characterizing depositional environments. Increasing content of pyrite in siltstones, detected by CT scanning and X-ray fluorescence spectroscopy, indicates deeper and less oxic conditions, while the presence of carbonate shell debris indicates shallower, more oxic depositional settings. The NMR test results show that the NMR signal distribution is affected by both pore size distribution and mineralogical composition. An increase of pyrite content caused shifting of the T2 distribution to the lower values, while carbonate inclusions caused shifting of the T2 distribution to higher values relative to the other samples not affected by these mineral inclusions. Because NMR distribution is affected by multiple factors, applying Т2cutoff values alone for rock typing can lead to ambiguous interpretation. Applying CT scanning next to NMR data increases the reliability of rock typing. The proposed laboratory workflow, including a combination of nonhazardous and nondestructive tests, allowed reliable differentiation of the rock samples based on multiple parameters that were interpreted in relationship with each other. Because the designed laboratory test workflow enabled both justified separation of the samples by rock type and determination of parameters used for reserves calculation, it can be recommended for further application in polymineral clastic reservoirs. Because the proposed techniques are nondestructive, the same samples can be applied for multiple tests including special core analysis (or SCAL).


2021 ◽  
pp. 1-14
Author(s):  
Yongzan Liu ◽  
Ge Jin ◽  
Kan Wu

Summary Rayleigh frequency-shift-based distributed strain sensing (RFS-based DSS) is a fiber-optic-based diagnostic technique, which can measure the strain change along the fiber. The spatial resolution of RFS-based DSS can be as low as 0.2 m, and the measuring sensitivity is less than 1 μɛ. Jin et al. (2021) presented a set of DSS data from the Hydraulic Fracture Test Site 2 project to demonstrate its potential to characterize near-wellbore fracture properties and to evaluate perforation efficiency during production and shut-in periods. Extensional strain changes are observed at locations around perforations during a shut-in period. At each perforation cluster, the observed responses of strain changes are significantly different. However, the driving mechanisms for the various observations are not clear, which hinders accurate interpretations of DSS data for near-wellbore fracture characterization. In this study, we applied a coupled flow and geomechanics model to simulate the observed DSS signals under various fractured reservoir conditions. The objective is to improve understanding of the DSS measurements and characterize near-wellbore fracture geometry. We used our in-house coupled flow and geomechanics simulator, which is developed by a combined finite-volume and finite-element method, to simulate strain responses within and near a fracture during shut-in and reopen periods. Local grid refinement was adopted around fractures and the wellbore, so that the simulated strain data can accurately represent the DSS measurements. The plane-strain condition is assumed. Numerical models with various fracture geometries and properties were constructed with representative parameters and in-situ conditions of the Permian Basin. The simulated well was shut-in for 4 days after producing 240 days, and reopened again for 1 day, following the actual field operation as shown in Jin et al. (2021). The characters of the strain changes along the fiber were analyzed and related to near-wellbore fracture properties. A novel diagnostic plot of relative strain change vs. wellbore pressure was presented to infer near-wellbore fracture characteristics. The impacts of permeability and size of the near-wellbore-stimulated region, fracture length, and near-perforation damage zone on strain responses were investigated through sensitivity analysis. The strain responses simulated by our model capture the observed signatures of field DSS measurements. During the shut-in period, clear positive strain changes are observed around the perforation locations, forming a “hump” signature. The shape of the “hump” region and peak value of each “hump” are dependent on the size and permeability of the near-wellbore fractured zone. Once the well is reopened, the strain changes decrease as the pressure drops. However, in one cycle of shut-in and reopen, the strain-pressure diagnostic plot shows path dependency. The discrepancy between the shut-in and reopen periods is highly influenced by the properties of near-wellbore fractured zones. The differences in the strain-pressure diagnostic plots can help to identify the conductive fractures. This study provides better understandings of the DSS measurements and their relations to the near-wellbore fracture properties, which is of practical importance for near-wellbore fracture characterization and completion/stimulation optimization.


2021 ◽  
pp. 1-16
Author(s):  
Wei Shao ◽  
Songhua Chen ◽  
Gabor Hursan ◽  
Shouxiang Ma

Summary Nuclear magnetic resonance (NMR)-based interpretation models are commonly calibrated using laboratory ambient core NMR measurements. For applying the core calibrated models to downhole NMR logging interpretation, the difference between the NMR responses measured at ambient and reservoir temperature needs to be evaluated. The temperature dependence of NMR relaxation time in high-quality (HQ) carbonate reservoirs has been studied, and NMR temperature dependence models were established using data analytic methods. In this paper, we extend our early studies on temperature dependence of NMR relaxation time to low-quality (LQ) carbonate formations. For more than 95% of the LQ samples investigated, NMR relaxation time shows a positive correlation with temperature. The correlation is similar to that observed in HQ carbonate rocks but slightly less significant. Temperature-dependent correlations for predicting the geometric mean of NMR transverse relaxation time (T2,GM) from a measured temperature to any other temperature were derived from HQ to LQ carbonate rocks independently first, then a unified T2,GM correlation was derived including both the HQ and LQ carbonate reservoirs. Predicting NMR transverse relaxation time T2 distribution from one temperature to other temperatures was achieved using a dimension reduction approach involving the principal component analysis (PCA) technique. It was found that the T2 distributions at any given temperature for both HQ and LQ carbonate reservoirs can be predicted robustly from the T2 distributions at the ambient temperature by representing the T2 distributions with principal components (PCs) at the ambient temperature and then using these PCs to predict the PCs at a different temperature. The optimal number of PC components depends on the multimodality of the T2distribution. This work extends the validity range of the data analytic methods, in particular parameter and dimension reduction methods, that quantify the temperature dependence of carbonate NMR properties. The new NMR temperature model enables the integration of NMR laboratory studies and downhole measurements for advanced petrophysical analyses in a wide range of carbonate reservoirs.


2021 ◽  
pp. 1-21
Author(s):  
M. Kowsari ◽  
L. A. James ◽  
R. D. Haynes

Summary Water-alternating gas (WAG) as a tertiary recovery method is applied to oil reservoirs at a later stage of reservoir life to more or less success depending on field and operation. Uncertainty in WAG optimization has been shown to be dependent on several factors including reservoir characterization, WAG timing, and its operation. In this paper, we comprehensively explore WAG optimization in the context of WAG operating parameters and hysteresis, the first paper to explore both simultaneously. WAG operating parameters have been analyzed and optimized at both the core and field scale with a general conclusion that the timing, miscibility, WAG ratio, cycle time, and number of cycles play a varying role in the WAG optimization. Reservoir characterization has considered well configuration, oil type, rock properties, and hysteresis in relative permeability. Due to the cyclic nature of WAG and the dependency of the relative permeability on the saturation history, the relative permeability hysteresis modeling plays a key role in WAG performance whereby different hysteresis models will predict different results, as shown in literature. In this paper, we consider the choice of the hysteresis model and WAG operating parameters on WAG optimization. First, a sensitivity analysis is performed to evaluate the effect of hysteresis models (no hysteresis, Carlson, and Killough) on a large number of WAG development scenarios sampled by the Latin hypercube sampling method. Next, optimizations were conducted to compare and analyze the optimum recovery factor and corresponding optimal WAG operating parameters for various combinations of hysteresis models. The results of the study indicate that excluding hysteresis modeling from simulations would likely lead to a higher predicted produced volume of the nonwetting phases, that is, oil and gas, and a lower predicted produced volume of the wetting phase, that is, water. Also, the optimal recovery factor as well as the optimal WAG operating parameters can be significantly affected by the choice of the hysteresis models.


2021 ◽  
pp. 1-15
Author(s):  
Xiao Jin ◽  
Alhad Phatak ◽  
Aaron Sanders ◽  
Dawn Friesen ◽  
Ed Lewis ◽  
...  

Summary In mixed- to oil-wet reservoirs characterized by intense natural fracturing where the dominant displacement mechanism is gravity drainage, surfactant injection can lead to a shift in wettability and incremental oil production. In some cases, oil can also reimbibe back into the rock matrix after the oil saturation has been reduced upon initial exposure to surfactant, suggesting limited permanence in the wettability shift. The reimbibition phenomenon is investigated in this paper using Amott cells. Three cationic surfactants (C12-, C12–16-, C16-based) with interfacial tensions (IFT) between 0.18 and 0.95 mN/m were preselected to be evaluated. Current application of the C12-based surfactant in the Yates field is considered successful based on incremental oil recovery seen during the treatment. Silurian dolomite (SD) rock samples were flooded with Yates crude oil before being aged at 60°C for 6 weeks. For the imbibition tests, the aqueous surfactant solution was set as the external phase within the Amott cell, and the recovery of oil was recorded periodically. After the imbibition tests ended, the rock samples were placed in an inverse Amott cell with the Yates oil as the external phase. Baseline tests were first conducted to show that without a surfactant in the oil or brine, no imbibition occurred. With a surfactant concentration of 3,000 ppm, oil recovery at the end of the imbibition tests varied from 34 to 60% of the original oil volume in the core sample. During the reimbibition test, a large amount of oil was able to reimbibe into the rock, displacing the brine. Most of the displacement occurred within the first 2 weeks. The net oil recovery, taken as the final volume of oil recovered in the imbibition test minus the final volume of oil reimbibed into the rock, ranged from 0 to 18%. Given the possibility of surfactant dilution in field applications, another set of tests was conducted with 1,500 ppm. A reduction in oil recovery during imbibition was observed for all the tested surfactants. Partition coefficients were determined for each of the tested surfactants, and the ion-pair mechanism was used to explain the net oil recovery results. Lastly, the impact of rock permeability on reimbibition was investigated. Results show increasing permeability may lead to a linear response in oil reimbibition; therefore, minimizing the permeability range when selecting rock samples may be necessary when conducting the reimbibition test. The importance of oil reimbibition is demonstrated in the experimental study, and we make an argument for conducting both the imbibition and reimbibition tests to better evaluate surfactant efficacy. The improved understanding of wettability alteration should lead to advancements in chemical enhanced oil recovery (EOR) designs for field treatments.


2021 ◽  
pp. 1-20
Author(s):  
Ziming Xu ◽  
Juliana Y. Leung

Summary The discrete fracture network (DFN) model is widely used to simulate and represent the complex fractures occurring over multiple length scales. However, computational constraints often necessitate that these DFN models be upscaled into a dual-porositydual-permeability (DPDK) model and discretized over a corner-point grid system, which is still commonly implemented in many commercial simulation packages. Many analytical upscaling techniques are applicable, provided that the fracture density is high, but this condition generally does not hold in most unconventional reservoir settings. A particular undesirable outcome is that connectivity between neighboring fracture cells could be erroneously removed if the fracture plane connecting the two cells is not aligned along the meshing direction. In this work, we propose a novel scheme to detect such misalignments and to adjust the DPDK fracture parameters locally, such that the proper fracture connectivity can be restored. A search subroutine is implemented to identify any diagonally adjacent cells of which the connectivity has been erroneously removed during the upscaling step. A correction scheme is implemented to facilitate a local adjustment to the shape factors in the vicinity of these two cells while ensuring the local fracture intensity remains unaffected. The results are assessed in terms of the stimulated reservoir volume calculations, and the sensitivity to fracture intensity is analyzed. The method is tested on a set of tight oil models constructed based on the Bakken Formation. Simulation results of the corrected, upscaled models are closer to those of DFN simulations. There is a noticeable improvement in the production after restoring the connectivity between those previously disconnected cells. The difference is most significant in cases with medium DFN density, where more fracture cells become disconnected after upscaling (this is also when most analytical upscaling techniques are no longer valid); in some 2D cases, up to a 22% difference in cumulative production is recorded. Ignoring the impacts of mesh discretization could result in an unintended reduction in the simulated fracture connectivity and a considerable underestimation of the cumulative production.


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