Integrated Field-Scale Production and Economic Evaluation Under Subsurface Uncertainty for the Pattern-Driven Development of Unconventional Resources With Analytical Superposition

2015 ◽  
Vol 19 (01) ◽  
pp. 118-129 ◽  
Author(s):  
Guohua Gao ◽  
Jeroen C. Vink ◽  
Faruk O. Alpak
Keyword(s):  
Time Delay ◽  
Work Flow ◽  
Scale Production ◽  
Field Scale ◽  
Full Field ◽  
Unconventional Resources ◽  
Single Pattern ◽  
Pattern Development ◽  
The Impact

Summary The in-situ upgrading process (IUP) is a thermal-recovery technique that relies on a pattern-based development process, a complicated physical process that involves thermal and mass transfer in porous media, which renders full field-scale reservoir simulations impractical. Although it is feasible to quantify the impact of subsurface uncertainties on recovery for small-scale sector models with experimental design (ED), it is still a very challenging problem to quantify their impact on field-scale quantities. Straightforward upscaling to field scale does not work because such conventional superposition-based methods do not capture the effects of spatial variability in rock and fluid properties and the time delay in sequential pattern development. In this paper, we show that, under certain mild assumptions, an analytical superposition formulation can be developed that propagates the uncertainties of production forecasts and economic evaluations generated from a sector model to full field-scale quantities. One can simplify this formulation further so that the variance of a field-scale quantity is analytically expressed as the variance of the same single-pattern quantity multiplied by a (computable) scaleup factor. This makes it possible to implement a practical uncertainty quantification work flow in which single-pattern results are upscaled to accurate full field results with reliable uncertainty ranges, without the need for full field-scale simulations. We apply the proposed novel superposition and uncertainty-propagation method to a multipattern IUP development, and demonstrate that this work flow produces reliable results for field-scale production and economics as well as realistic uncertainty ranges. Moreover, these results indicate that the scaleup factor for single-pattern results can accurately capture the impact of spatial correlations of subsurface uncertainties, the size of the field-scale model, the time-delay in pattern development, and the discount rate. Uncertainty quantification of field-scale production and economics is a key factor for the successful development of unconventional resources such as extraheavy oil and oil shale with significant rewards in terms of risk management and project profitability. With minor modifications, the proposed method can also be applied to other pattern-driven processes such as the in-situ conversion process (ICP) and steam-assisted gravity drainage.

An Efficient Optimization Work Flow for Field-Scale In-Situ Upgrading Developments

SPE Journal ◽  
2015 ◽  
Vol 20 (04) ◽  
pp. 701-716 ◽  
Author(s):  
Guohua Gao ◽  
Jeroen C. Vink ◽  
Faruk O. Alpak ◽  
W.. Mo
Keyword(s):  
Net Present Value ◽  
Optimization Method ◽  
Training Data ◽  
Work Flow ◽  
Surface Model ◽  
Field Scale ◽  
Full Field ◽  
Single Pattern ◽  
Gradient Based

Summary In-situ upgrading process (IUP) is an attractive technology for developing unconventional extraheavy-oil reserves. Decisions are generally made on field-scale economics evaluated with dedicated commercial tools. However, it is difficult to conduct an automated IUP optimization process because of unavailable interface between the economic evaluator and commercial simulator/optimizer, and because IUP is such a highly complex process that full-field simulations are generally not feasible. In this paper, we developed an efficient optimization work flow by addressing three technical challenges for field-scale IUP developments. The first challenge was deriving an upscaling factor modeled after analytical superposition formulation; proposing an effective method of scaling up simulation results and economic terms generated from a single-pattern IUP reservoir-simulation model to field scale; and validating this approach numerically. The second challenge was proposing a response-surface model (RSM) of field economics to analytically compute key field economical indicators, such as net present value (NPV), by use of only a few single-pattern economic terms together with the upscaling factor, and validating this approach with a commercial tool. The proposed RSM approach is more efficient, accurate, and convenient because it requires only 15–20 simulation cases as training data, compared with thousands of simulation runs required by conventional methods. The third challenge is developing a new optimization method with many attractive features: well-parallelized, highly efficient and robust, and with a much-wider spectrum of applications than gradient-based or derivative-free methods, applicable to problems without any derivative, with derivatives available for some variables, or with derivatives available for all variables. This work flow allows us to perform automated field IUP optimizations by maximizing a full-field economics target while honoring all field-level facility constraints effectively. We have applied the work flow to optimize the IUP development of a carbonate heavy-oil asset. Our results show that the approach is robust and efficient, and leads to development options with a significantly improved field-scale NPV. This work flow can also be applied to other kinds of pattern-based field developments of shale gas and oil, and thermal processes such as steamdrive or steam-assisted gravity drainage.

Helical Boundary Conditions To Capture Interpattern Flow in In-Situ-Upgrading-Process Pattern Simulations

SPE Journal ◽  
2016 ◽  
Vol 21 (02) ◽  
pp. 393-404 ◽  
Author(s):  
Jeroen C. Vink ◽  
Guohua Gao ◽  
Jean-Charles C. Ginestra
Keyword(s):  
Time Delay ◽  
Boundary Conditions ◽  
Production Rate ◽  
Field Scale ◽  
Field Boundary ◽  
Current Pattern ◽  
Full Field ◽  
Sector Model ◽  
Simulation Results

Summary The in-situ upgrading process (IUP) involves very complicated multiphase thermal transport and chemical reactions. Numerical simulation of IUP is computationally expensive, and direct simulation of field-scale IUP models becomes prohibitive. A practical way is to simulate a sector model under proper boundary and initial conditions and then upscale the simulation results to the full field scale with superposition techniques. Because of nonsymmetric pattern configuration and time delay of developing patterns sequentially, interpattern flow may become significant, and its impact on the simulation results cannot be neglected. Therefore, no-flow boundary conditions become inappropriate for such IUP sector models. In this paper, we proposed a new type of “helical” boundary conditions (HBCs) in which pressures and temperatures are periodic in space, except for a shift in time. The HBCs are specifically designed for a field-scale IUP development in which patterns are developed sequentially in time, along a long strip. In such a development, each pattern has exactly the same well configuration and operational schedule, except for a time delay. Because of high viscosity of heavy oil and low heat conductivity of formation rock, the impact of field boundary conditions on interpattern flow will be dampened quickly within only a few patterns, and a repetitive “pseudosteady state” of interpattern flow develops, in which the energy and mass fluxes from the previous pattern to the current pattern are the same as those from the current pattern to the next pattern, except for the delay time. By use of a 1D heat-transfer model, we analytically demonstrate that the pseudosteady state and therefore the HBCs hold for a long strip of patterns. A practical procedure to implement these HBCs in numerical simulation is developed, in which the state variables (pressure, temperature, and fluid composition) are iteratively updated in gridblocks on both edges of a sector model that is composed of two patterns. This iterative approach to impose HBCs was implemented in our in-house simulator. This approach was tested and validated by simulation results of an IUP model that is composed of 59 patterns. Our results show that the full-field boundary conditions only affect the production-rate profiles of the first and the last patterns. Production-rate profiles generated from all other patterns are almost identical except for the interpattern time delay, which also validates the pseudosteady state of interpattern flow for a more-complicated IUP model. The two-pattern sector model with the HBCs converges in three to four iterations. The production-rate profiles of oil, gas, and water generated by the sector model with HBCs are almost identical to those produced from one of those inner patterns in the 59-pattern model. With the 1D example, we also analytically demonstrate the convergence of our numerical implementation of HBCs. In terms of clock-time used, it is possible to achieve 5N time speedup through application of the HBCs, in which N is the number of patterns in a field-scale model. Therefore, the new approach is proved a key enabler for field-scale IUP pattern optimization. Provided that the interpattern-pressure communication that is induced by the pattern delay time is not too severe, we expect that one can also apply HBCs to the simulation of other field-scale thermal processes, such as the in-situ conversion process and steamfloods.

scholarly journals Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region

2021 ◽  
Vol 13 (14) ◽  
pp. 2667
Author(s):  
Nadia Ouaadi ◽  
Lionel Jarlan ◽  
Saïd Khabba ◽  
Jamal Ezzahar ◽  
Michel Le Page ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Use ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Field Scale ◽  
Method Performance ◽  
The Impact ◽  
Statistical Metrics

Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = −18.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.

scholarly journals In Situ Monitoring of Additive Manufacturing Using Digital Image Correlation: A Review

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1511
Author(s):  
Filipa G. Cunha ◽  
Telmo G. Santos ◽  
José Xavier
Keyword(s):  
Additive Manufacturing ◽  
Digital Image Correlation ◽  
Digital Image ◽  
Field Measurements ◽  
In Situ Monitoring ◽  
Image Correlation ◽  
Full Field ◽  
The Impact ◽  
Am Processes

This paper is a critical review of in situ full-field measurements provided by digital image correlation (DIC) for inspecting and enhancing additive manufacturing (AM) processes. The principle of DIC is firstly recalled and its applicability during different AM processes systematically addressed. Relevant customisations of DIC in AM processes are highlighted regarding optical system, lighting and speckled pattern procedures. A perspective is given in view of the impact of in situ monitoring regarding AM processes based on target subjects concerning defect characterisation, evaluation of residual stresses, geometric distortions, strain measurements, numerical modelling validation and material characterisation. Finally, a case study on in situ measurements with DIC for wire and arc additive manufacturing (WAAM) is presented emphasizing opportunities, challenges and solutions.

Mangala Polymer Flood Performance: Connecting the Dots Through in Situ Polymer Sampling

2021 ◽  
Author(s):  
Vivek Shankar ◽  
Shekhar Sunit ◽  
Alasdair Brown ◽  
Abhishek Kumar Gupta
Keyword(s):  
Prolonged Exposure ◽  
Polymer Degradation ◽  
Accelerated Ageing ◽  
Degree Of Hydrolysis ◽  
Full Field ◽  
Polymer Sample ◽  
Polymer Flood ◽  
Reservoir Conditions ◽  
The Impact

Abstract The paper describes the in-situ polymer sampling in Mangala which helped explain the performance of a large polymer flood in Mangala field in India. The Mangala field contains medium-gravity viscous crude oil. Notably, it is the largest polymer flood in India and 34% of the STOIIP has been produced in 11 years of production. Mangala was put on full field polymer flood in 2015, six years after the start of field production on water flood in 2009. Polymer flood added 93 million barrels above the anticipated water flood recovery in 6 years. Reservoir simulation models could replicate the initial Mangala polymer flood performance. However, the performance of the lower layers of Mangala (FM-3 and FM-4) continued to progressively deviate from modeling estimates. Equally importantly, the prediction of polymer breakthrough deviated significantly from modeling estimates. After 6 years and 0.7 pore volumes of polymer injection, it is apparent that field performance is equivalent to only 50-60% of the viscosity of the polymer injected at the surface. To better understand and quantify the nature and extent of polymer degradation it is necessary to gather representative down hole samples of polymer which has stayed in the reservoir conditions for a considerable length of time. Accelerated ageing studies in the lab showed HPAM can lose viscosity and precipitate after prolonged exposure to Mangala reservoir conditions with an increase in the degree of hydrolysis as the primary reason for the degradation. The concept of transfer function based on first order kinetics was used to extrapolate the laboratory results to Mangala reservoir temperatures. To test the hypothesis, a multi-disciplinary team implemented a plan to gather a representative polymer sample from the reservoir. The polymer sample had been in the reservoir for nearly 120 days and was captured in low shear and anaerobic conditions to minimize shear and oxidative degradation. The sample was tested for degree of hydrolysis by NMR method. The results confirmed that the level of hydrolysis of the injected HPAM did increase in the reservoir leading to lower viscosity and reduced lower amide concentration. Preliminary simulation studies using the concept of viscosity half-life were used to mimic the polymer degradation with time in the reservoir. The method is quite a simplistic representation of the thermal degradation, but it significantly improved the model's water cut predictions for lower layers and the full field polymer breakthrough predictions. The impact of polymer precipitation in the reservoir on the permeability is under study and it will drive the next phase of more detailed modeling.

In situ Quantification of the Impact of Episodic Enhanced Turbulent Events in Large Phytoplankton

2011 ◽  
Author(s):  
Percy L. Donaghay ◽  
Jan Rines ◽  
James Sullivan
Keyword(s):  
The Impact

Bayesian analysis reveals the impact of load partitioning on microstructural evolution in Ti-6Al-4V during in-situ tensile loading

Materialia ◽  
2021 ◽  
Vol 15 ◽  
pp. 100993
Author(s):  
N. Armstrong ◽  
P.A. Lynch ◽  
P. Cizek ◽  
S.R. Kada ◽  
S. Slater ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Microstructural Evolution ◽  
Tensile Loading ◽  
The Impact

scholarly journals Solid-Phase Extraction Embedded Dialysis (SPEED), an Innovative Procedure for the Investigation of Microbial Specialized Metabolites

Marine Drugs ◽  
2021 ◽  
Vol 19 (7) ◽  
pp. 371
Author(s):  
Phuong-Y Mai ◽  
Géraldine Le Goff ◽  
Erwan Poupon ◽  
Philippe Lopes ◽  
Xavier Moppert ◽  
...  
Keyword(s):  
Solid Phase Extraction ◽  
Solid Phase ◽  
Chemical Space ◽  
Molecular Profile ◽  
Phase Extraction ◽  
Specialized Metabolites ◽  
Streptomyces Albidoflavus ◽  
Cultivation Process ◽  
The Impact

Solid-phase extraction embedded dialysis (SPEED technology) is an innovative procedure developed to physically separate in-situ, during the cultivation, the mycelium of filament forming microorganisms, such as actinomycetes and fungi, and the XAD-16 resin used to trap the secreted specialized metabolites. SPEED consists of an external nylon cloth and an internal dialysis tube containing the XAD resin. The dialysis barrier selects the molecular weight of the trapped compounds, and prevents the aggregation of biomass or macromolecules on the XAD beads. The external nylon promotes the formation of a microbial biofilm, making SPEED a biofilm supported cultivation process. SPEED technology was applied to the marine Streptomyces albidoflavus 19-S21, isolated from a core of a submerged Kopara sampled at 20 m from the border of a saltwater pond. The chemical space of this strain was investigated effectively using a dereplication strategy based on molecular networking and in-depth chemical analysis. The results highlight the impact of culture support on the molecular profile of Streptomyces albidoflavus 19-S21 secondary metabolites.

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