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Results
Abstract When simulating foam floods, uncertainties exist in both the foam and reservoir parameters however the combination of these uncertainties are rarely incorporated in forecasting. Foam flooding is an effective enhanced oil recovery method that controls mobility, reduces gas relative permeability, delays gas breakthrough and helps improve sweep efficiency. Thus it is often used in highly heterogeneous reservoirs where significant subsurface uncertainties exist. Foam uncertainties exist as (a) foam stability is controlled by a number of factors such as critical water and surfactant concentrations, brine salinity, and oil saturation which are unknown in the subsurface spatially and (b) foam flood simulation requires the accurate description of multiple parameters used in the foam flood models which are unknown. This study quantifies and compares the impact of uncertainties associated with foam model parameters with the heterogeneity of a fractured carbonate reservoir, an analogue to the highly prolific Arab D formation. Foam model parameters are not known a-priori but can be tuned to experimental data, which ideally represent a range of foam regimes and reservoir conditions. Geological heterogeneities in fractured carbonate reservoirs are complex and include, matrix wettability, fracture density/orientation and initial saturation distribution. To quantify uncertainties geological uncertainties in fractured carbonate reservoirs, an automated framework was used to history match the production response of a fractured carbonate field by varying geological parameters. This accounts for the geological uncertainties during a waterflood, which are then combined with foam uncertainties from experimental analysis in the optimisation step, by optimising the mean response of the model to foam flooding across a range of geological and foam scenarios. Our workflow used a combination of Particle Swarm Optimisation for history matching and manual optimisation, the final results of which show a wide range of possible impacts of foam flooding from different but equally well matched reservoirs. The novelty of our work is that it demonstrates how parameters that control foam stability and hence effectiveness in mobility control are related to both foam properties and geological uncertainty. Carrying these uncertainties into foam model properties from core to field scale will translate into considerably more robust estimates of uncertainty when predicting field-scale recovery compared to simulations that only consider uncertainty in the reservoir model.
- North America > Canada (0.28)
- Asia > Middle East (0.28)
- Research Report > New Finding (0.66)
- Research Report > Experimental Study (0.66)
- Geology > Petroleum Play Type > Unconventional Play > Fractured Carbonate Reservoir Play (1.00)
- Geology > Rock Type (0.95)
- Geology > Structural Geology > Fault (0.93)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 375 > Block 34/7 > Snorre Field > Statfjord Group (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 375 > Block 34/7 > Snorre Field > Lunde Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 375 > Block 34/4 > Snorre Field > Statfjord Group (0.99)
- (9 more...)
Abstract The objective of this paper is to reduce the computational effort in reservoir flooding optimization problems by a combination of different optimization parametrization methods and model order reduction techniques. We compare three different parametrization methods that reduce the cardinality of the original infinite set of control-decision variables to a finite set. The three methods include a traditional piece-wise constant (PWC) approximation, a polynomial approximation by Chebyshev orthogonal polynomials and a piece-wise polynomial approximation by cubic Spline interpolation. The Proper Orthogonal Decomposition with Discrete Empirical Interpolation Method (POD-DEIM) accomplishes the reduced order modeling (ROM).. We compare a gradient-free global stochastic search approach and a gradient-based local search approach. We used Particle Swarm Optimization (PSO) as a gradient-free algorithm and Interior-Point Optimization (IPOPT) with L-BFGS method as a gradient-based algorithm. First, we compare the performances of the three parametrization methods solved by each optimizer, using fine scale simulations for an increasing level of parametrization refinement. Then, in the second part of this paper, we combine the parametrization methods with the reduced modeling workflow. For a given level of parametrization refinement, we compare the performance of each parametrization method coupled with POD-DEIM, and solved by each optimizer. In this part, we introduce an online training procedure, where the first optimization iteration is used to construct the snapshot matrix. The results demonstrate how refining the control approximation with more decision variables per well lead to better NPV values, but with a higher computational cost. The best NPV was achieved using the highest refining level with Chebyshev polynomial approximation. Both polynomial and piece-wise polynomial approximations served as better training sets for POD-DEIM leading to a more accurate and fast reduced model. With the strategy proposed, POD-DEIM showed the best optimization accuracy for Chebyshev polynomial with the gradient-free optimizer, thus permitting the use of the model reduction methodology for global-stochastic search methods. However, the gradient-based approach seems to consistently outperform the gradient-free approach in terms of NPV and number of iterations for the cases shown.
Integrated interpretation of 2D ground-penetrating radar, P-, and S-wave velocity models in terms of petrophysical properties: Assessing uncertainties related to data inversion and petrophysical relations
Tronicke, Jens (University of Potsdam) | Paasche, Hendrik (UFZ โ Helmholtz Center for Environmental Research)
Abstract Near-surface geophysical techniques are extensively used in a variety of engineering, environmental, geologic, and hydrologic applications. While many of these applications ask for detailed, quantitative models of selected material properties, geophysical data are increasingly used to estimate such target properties. Typically, this estimation procedure relies on a two-step workflow including (1)ย the inversion of geophysical data and (2)ย the petrophysical translation of the inverted parameter models into the target properties. Standard deterministic implementations of such a quantitative interpretation result in a single best-estimate model, often without considering and propagating the uncertainties related to the two steps. We address this problem by using a rather novel, particle-swarm-based global joint strategy for data inversion and by implementing Monte Carlo procedures for petrophysical property estimation. We apply our proposed workflow to crosshole ground-penetrating radar, P-, and S-wave data sets collected at a well-constrained test site for a detailed geotechnical characterization of unconsolidated sands. For joint traveltime inversion, the chosen global approach results in ensembles of acceptable velocity models, which are analyzed to appraise inversion-related uncertainties. Subsequently, the entire ensembles of inverted velocity models are considered to estimate selected petrophysical properties including porosity, bulk density, and elastic moduli via well-established petrophysical relations implemented in a Monte Carlo framework. Our results illustrate the potential benefit of such an advanced interpretation strategy; i.e., the proposed workflow allows to study how uncertainties propagate into the finally estimated property models, while concurrently we are able to study the impact of uncertainties in the used petrophysical relations (e.g., the influence of uncertain, user-specified parameters). We conclude that such statistical approaches for the quantitative interpretation of geophysical data can be easily extended and adapted to other applications and geophysical methods and might be an important step toward increasing the popularity and acceptance of geophysical tools in engineering practice.
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Electromagnetic Surveying (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (0.89)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.67)
Software & hardware platform for Digital Oilfield system organization (Russian)
Korovin, I. S. (Scientific Research Institute of Multiprocessor Computer Systems at Southern Federal University, RF, Taganrog) | Tkachenko, M. G. (Scientific Research Institute of Multiprocessor Computer Systems at Southern Federal University, RF, Taganrog)
The PDF file of this paper is in Russian. Approach of building a software-hardware platform applied for solution of production tasks in the Digital Oilfield framework is considered in the paper. Nowadays a problem of development and implentation into industrial processes a united digital oilfield system assumes proceeding a sufficient amounts of work, linked with the integration of novel software & hardware tools into the existing infrastructure of an oil company. The suggested way of solving the given problem a priori demands huge financial investments and leads to unevitable stops in the work of the oil well fund, that is inadmissible in real industrial conditions. In the paper we suggested an approach of building an intelligent oilfield system on the basis of autonomous software-hardware modules, phase-by-phase implemented into the existing corporate control systems. Also, we offered to apply data mining techniques, in particular, artificial neural networks, and also distributed calculations technology on the multiagent interaction for real time data handling procedures optimization. A prototype of a hardware-software module is presented.
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.69)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.56)
- Information Technology > Architecture > Real Time Systems (0.55)
A Novel Methodology to Optimise the Parameters of Hydraulic Fracturing in Gas Condensate Reservoirs
Tariq, Zeeshan (King Fahd University of Petroleum & Minerals) | Al-Hashim, Hasan S. (King Fahd University of Petroleum & Minerals) | Sadeed, Ahmed (King Fahd University of Petroleum & Minerals) | Janjua, Aneeq Nasir (King Fahd University of Petroleum & Minerals)
When the pressure falls below the dew point in a condensate reservoir, condensate banking starts build up adjacent to the wellbore, the relative permeability of gas closes to the wellbore badly affected, ultimately causes the reduction in the well productivity index. In these cases, the influence of condensate blockage can be abridged by enhancing the inflow area and attaining linear flow rather than radial flow in the wellbore proximity. These characteristics can only be accomplished by conducting hydraulic fracturing job. However, the performance of hydraulic fracturing treatment is highly dependent on the operational parameters. Estimation of optimal operational parameters of hydraulic fracturing is typically a difficult job for reservoir engineers. Enhancing the fracture half-length improves the well productivity but this scenario is misleading sometime. Fracture face skin is always associated with the fracture length that may induce additional pressure drop surrounding the fracture and can cause the gas condensate buildup. This happen because as the length of the fracture increases the damage surrounds the fracture cause by permeability impairment also increases, so there must be an optimum fracture length above which increasing the length of fracture is no more efficacious. This research work presents a new methodology to quantify the amount of fracture face skin associated with the length of the fracture and determine optimum parameter for hydraulic fracturing in a gas condensate reservoir. To achieve the objective of this study a gas condensate simulation model has been created and the parameters associated with the hydraulic fracturing has been deeply investigated, particle swarm optimization has been used as an optimization technique to optimize the parameters that has been investigated. After optimizing the parameters, the new approach has been proposed that suggests the optimum fracture parameters. This new approach is highly promising and can serve as very handy tool for reservoir engineers to determine the optimum operational hydraulic fracturing treatment parameters for a gas condensate reservoir.
- Overview (0.54)
- Research Report > New Finding (0.46)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 479 > Block 6506/12 > ร sgard Field > Smรธrbukk Field > ร re Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 479 > Block 6506/12 > ร sgard Field > Smรธrbukk Field > Tofte Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 479 > Block 6506/12 > ร sgard Field > Smรธrbukk Field > Tilje Formation (0.99)
- (2 more...)
Abstract This paper suggests a new Magnetic Particle Imaging (MPI) technique for two dimensional (2D) tomographic imaging to detect the presence of flowing solid particles (i.e. black powder which consist of paramagnetic nanoparticles (MNPs)) passing through gas-oil pipelines. The hardware consists of a ring of a modified Halbach array of 24 permanent magnets, in addition to two surrounding Helmholtz coil pairs, and a receiving coil, which are evenly distributed across a cross-section of the probe. With the application of static and dynamically moving drive fields, the MPI utilizes the full benefit of superparamagnetic iron oxide nanoparticles (SPIONs) by providing a linear response when they are exposed to a relatively low magnetic field and no response when they are saturated. The scanning of region of interest with the field-free line (FFL) instead of the field-free point (FFP) is motivated by the fact that a high intensity DC current is required for FFP, which is not tolerated in oil-gas pipelines where the maximal current should not exceed few Amps. With the presence of oscillatory magnetic field, SPIONs react with a non-linear magnetization response which is further measured by the receiving coil. A 2D Image reconstruction is then performed using frequency-based image reconstruction process. The hardware design, specifically the sizing of permanent magnets with regard to their relative position and dimensions, was refined following an optimization technique based on Particle Swarm Optimization (PSO) technique. The assessment of the system using finite element method indicates that the system can reconstruct the 2D profile of the SPOIN with a mean absolute error of prediction of less than 4 % and 12% using FFL and FFP methods respectively.
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)
Aged Carbonate Cores Wettability Verification
Kovalev, K.. (JSC VNIIneft) | Fomkin, A.. (JSC VNIIneft) | Grishin, P.. (JSC VNIIneft) | Kurochkin, A.. (JSC VNIIneft) | Kolesnikov, M.. (JSC VNIIneft) | Levchenko, A.. (JSC VNIIneft) | Afanasiev, I.. (JSC Zarubezhneft) | Fedorchenko, G.. (JSC Zarubezhneft)
Abstract With the question of reliability on the results of standard and SCAL tests on carbonate cores of the Central Khoreiver Uplift (CKU) reservoirs, a study was carried out to develop appropriate core preparation techniques for the rock. Several tests were carried out on core plugs with cleaning agents like: alcohol-benzene, toluene, chloroform, carbon tetrachloride in the Soxhlet apparatus, cold kerosene in centrifuge and hot kerosene in coreholder. Cleaning effects on porosity, gas permeability, wettability, irreducible water saturation were studied. The influence of each agent on basic core properties is shown. Based on the various cleaning techniques available and the results achieved, the most suitable cleaning method for CKU carbonates was recommended. Core aging techniques with dead oil at reservoir temperature and set pressure were carried out. Single aging and sequential multi aging at different aging periods and their effects on core properties were shown. Core preparation techniques, uncleaned core tests evaluation, aging methods and further studies were also recommended.
- North America > United States (1.00)
- Europe (1.00)
- North America > Canada > Alberta (0.28)
- Research Report > New Finding (0.66)
- Research Report > Experimental Study (0.48)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas > Upstream (1.00)
ABSTRACT The elastic parameters ( and density) of a multilayered earth can be derived from amplitude versus offset (AVO) data. The problem is non-unique nature, as different combinations of elastic parameters may yield the same AVO response. An attempt has been made to estimate the elastic parameters using a constrained AVO inversion in which the derived from the travel time data (unique in nature) is kept constant during the AVO inversion. Methods based on local linearization fail if the starting model is too far from the true model. FDR PSO, being very robust and proficient in dealing with highly non-linear problems, is used for both the travel time and AVO inversions yielding an improved methodology. The inversion scheme, applied on synthetic data (both noise free and 2% noise added) shows very promising results. Presentation Date: Tuesday, October 18, 2016 Start Time: 8:25:00 AM Location: Lobby D/C Presentation Type: POSTER
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.73)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (0.73)
A hybrid PSOGSA-based inversion of noise corrupted seismic data using singular spectrum-based time slice denoising
Priyadarshi, Shubham Kumar (Indian School of Mines, Dhanbad) | Maiti, Saumen (Indian School of Mines, Dhanbad) | Rekapalli, Rajesh (CSIR-National Geophysical Research Institute, Hyderabad) | Tiwari, Ram Krishna (CSIR-National Geophysical Research Institute, Hyderabad)
ABSTRACT The quality of the surface seismic data plays a very important role in successful reservoir identification. In reservoir geophysics, impedance inversion from noise-corrupted seismic data has always been a challenge. In this paper, we present the application of Time Slice Singular Spectrum Analysis (TSSSA) based de-noising as a pre-filtering for seismic inversion using a hybrid of Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA). We tested the performance of a hybrid PSOGSA based inversion on pure synthetic data, synthetic data with 30% added and its TSSSA de-noised output. The comparison of the inverted seismic data from these data sets was performed. We observed that the impedance computed from the de-noised output shows a good match with the true impedance, whereas the estimated impedance from noisy data deviated from the original value. The TSSSA algorithm provides a robust approach for seismic data de-noising and facilitates accurate impedance estimation using PSOGSA based inversion from noise corrupted seismic data. Presentation Date: Tuesday, October 18, 2016 Start Time: 3:20:00 PM Location: Lobby D/C Presentation Type: POSTER
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.90)
ABSTRACT Inversion for seismic impedance is an ill-posed and nonlinear problem. Hence inversion results are non-unique and unstable. Scholars have made great efforts in this research and recent years it has emerged more and more new non-linear inversion method with the application of the nonlinear inversion problems. Standard particle swarm optimization (PSO) is not appropriate when we use it for the post-stack impedance directly. So we come up with an improved particle swarm optimization to alleviate these problems for the post-stack impedance inversion. This improved particle swarm optimization is based on the swarm intelligence and probabilistic theory for global optimization. This paper applied this method in the observation data of post-stack impedance inversion. The results show that this improved particle swarm optimization algorithm is a global optimization algorithm with a better performance than standard PSO for post-stack impedance inversion. It is feasible and effective for impedance inversion problem. Presentation Date: Monday, October 17, 2016 Start Time: 4:10:00 PM Location: Lobby D/C Presentation Type: POSTER
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.48)
- Asia > China > Heilongjiang > Songliao Basin > Daqing Field > Yian Formation (0.99)
- Asia > China > Heilongjiang > Songliao Basin > Daqing Field > Mingshui Formation (0.99)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)