scholarly journals Specifying Quality of a Tight Oil Reservoir through 3-D Reservoir Modeling

2020 ◽  
pp. 3252-3265
Author(s):  
Nagham Jasim ◽  
Sameera M. Hamd-Allah ◽  
Hazim Abass
Keyword(s):  
History Matching ◽  
Simulation Models ◽  
Static Model ◽  
Model Construction ◽  
Reservoir Modeling ◽  
Oil Reservoir ◽  
Main Challenge ◽  
Static Modeling ◽  
Porosity And Permeability ◽  
Log Interpretation

Increasing hydrocarbon recovery from tight reservoirs is an essential goal of oil industry in the recent years. Building real dynamic simulation models and selecting and designing suitable development strategies for such reservoirs need basically to construct accurate structural static model construction. The uncertainties in building 3-D reservoir models are a real challenge for such micro to nano pore scale structure. Based on data from 24 wells distributed throughout the Sadi tight formation. An application of building a 3-D static model for a tight limestone oil reservoir in Iraq is presented in this study. The most common uncertainties confronted while building the model were illustrated. Such as accurate estimations of cut-off permeability and porosity values. These values directly affect the calculation of net pay thickness for each layer in the reservoir and consequently affect the target of estimating reservoir initial oil in place (IOIP). Also, the main challenge to the static modeling of such reservoirs is dealing with tight reservoir characteristics which cause major reservoir heterogeneity and complexities that are problematic to the process of modeling reservoir simulation. Twenty seven porosity and permeability measurements from Sadi/Tanuma reservoir were used to validate log interpretation data for model construction. The results of the history matching process of the constructed dynamic model is also presented in this paper, including data related to oil production, reservoir pressure, and well flowing pressure due to available production.

scholarly journals Refining Permeability Values for History-Matching of the Reservoir Simulation Model

2020 ◽  
Vol 20 (3) ◽  
pp. 223-230
Author(s):  
Margarita A. Smetkina ◽  
◽  
Oleg A. Melkishev ◽  
Maksim A. Prisyazhnyuk ◽  
◽  
...  
Keyword(s):  
Reservoir Simulation ◽  
History Matching ◽  
Simulation Models ◽  
Data Interpretation ◽  
Well Logging ◽  
Initial Permeability ◽  
Oil Field ◽  
Statistical Characteristics ◽  
Reservoir Model ◽  
Porosity And Permeability

Reservoir simulation models are used to design oil field developments, estimate efficiency of geological and engineering operations and perform prediction calculations of long-term development performances. A method has been developed to adjust the permeability cube values during reservoir model history-matching subject to the corederived dependence between rock petrophysical properties. The method was implemented using an example of the Bobrikovian formation (terrigenous reservoir) deposit of a field in the Solikamskian depression. A statistical analysis of the Bobrikovian formation porosity and permeability properties was conducted following the well logging results interpretation and reservoir modelling data. We analysed differences between the initial permeability obtained after upscaling the geological model and permeability obtained after the reservoir model history-matching. The analysis revealed divergences between the statistical characteristics of the permeability values based on the well logging data interpretation and the reservoir model, as well as substantial differences between the adjusted and initial permeability cubes. It was established that the initial permeability was significantly modified by manual adjustments in the process of history-matching. Extreme permeability values were defined and corrected based on the core-derived petrophysical dependence KPR = f(KP) , subject to ranges of porosity and permeability ratios. By using the modified permeability cube, calculations were performed to reproduce the formation production history. According to the calculation results, we achieved convergence with the actual data, while deviations were in line with the accuracy requirements to the model history-matching. Thus, this method of the permeability cube adjustment following the manual history-matching will save from the gross overestimation or underestimation of permeability in reservoir model cells.

scholarly journals Constructing Reservoir Flow Simulator Proxies Using Genetic Programming for History Matching and Production Forecast Uncertainty Analysis

2008 ◽  
Vol 2008 ◽  
pp. 1-13 ◽  
Author(s):  
Tina Yu ◽  
Dave Wilkinson ◽  
Alexandre Castellini
Keyword(s):  
Genetic Programming ◽  
History Matching ◽  
Flow Simulation ◽  
Simulation Models ◽  
Time Frame ◽  
Reservoir Modeling ◽  
Production Data ◽  
Production Forecast ◽  
Future Production

Reservoir modeling is a critical step in the planning and development of oil fields. Before a reservoir model can be accepted for forecasting future production, the model has to be updated with historical production data. This process is called history matching. History matching requires computer flow simulation, which is very time-consuming. As a result, only a small number of simulation runs are conducted and the history-matching results are normally unsatisfactory. This is particularly evident when the reservoir has a long production history and the quality of production data is poor. The inadequacy of the history-matching results frequently leads to high uncertainty of production forecasting. To enhance the quality of the history-matching results and improve the confidence of production forecasts, we introduce a methodology using genetic programming (GP) to construct proxies for reservoir simulators. Acting as surrogates for the computer simulators, the “cheap” GP proxies can evaluate a large number (millions) of reservoir models within a very short time frame. With such a large sampling size, the reservoir history-matching results are more informative and the production forecasts are more reliable than those based on a small number of simulation models. We have developed a workflow which incorporates the two GP proxies into the history matching and production forecast process. Additionally, we conducted a case study to demonstrate the effectiveness of this approach. The study has revealed useful reservoir information and delivered more reliable production forecasts. All of these were accomplished without introducing new computer simulation runs.

Characterization of Arbuckle-basement wastewater disposal system, Payne County, Northern Oklahoma

2019 ◽  
Vol 7 (4) ◽  
pp. SL19-SL36
Author(s):  
Gabriel L. Machado ◽  
Garrett J. Hickman ◽  
Maulin P. Gogri ◽  
Kurt J. Marfurt ◽  
Matthew J. Pranter ◽  
...  
Keyword(s):  
History Matching ◽  
Quantitative Estimation ◽  
Simulation Models ◽  
3D Models ◽  
Injection Rate ◽  
P Wave ◽  
Reservoir Properties ◽  
Wastewater Disposal ◽  
Arbuckle Group ◽  
Porosity And Permeability

Over the past eight years, north-central Oklahoma has experienced a significant increase in seismicity. Although the disposal of large volumes of wastewater into the Arbuckle Group basement system has been statistically correlated to this increased seismicity, our understanding of the actual mechanisms involved is somewhat superficial. To address this shortcoming, we initiated an integrated study to characterize and model the Arbuckle-basement system to increase our understanding of the subsurface dynamics during the wastewater-disposal process. We constructed a 3D geologic model that integrates 3D seismic data, well logs, core measurements, and injection data. Poststack-data conditioning and seismic attributes provided images of faults and the rugose top of the basement, whereas a modified-Hall analysis provided insights into the injection behavior of the wells. Using a Pareto-based history-matching technique, we calibrated the 3D models using the injection rate and pressure data. The history-matching process showed the dominant parameters to be formation-water properties, permeability, porosity, and horizontal anisotropy of the Arbuckle Group. Based on the pressure buildup responses from the calibrated models, we identified sealing and conductive characteristics of the key faults. Our analysis indicates the average porosity and permeability of Arbuckle Group to be approximately 7% and 10 mD, respectively. The simulation models also showed pockets of nonuniform and large pressure buildups in these formations, indicating that faults play an important role in fluid movement within the Arbuckle Group basement system. As one of the first integrated investigations conducted to understand the potential hydraulic coupling between the Arbuckle Group and the underlying basement, we evaluate the need for improved data recording and additional data collection. In particular, we recommend that operators wishing to pursue this type of analysis record their injection data on a daily rather than on an averaged basis. A more quantitative estimation of reservoir properties requires the acquisition of P-wave and dipole sonic logs in addition to the commonly acquired triple-combo logs. Finally, to better quantify flow units with the disposal reservoir, we recommend that operators acquire sufficient core to characterize the reservoir heterogeneity.

The Integration of Analytical P/Z Material Balance Method with Static Modeling and Integrated Asset Model IAM to Generate Reliable Forecasting for a Giant Onshore Gas Field in Abu Dhabi

2021 ◽  
Author(s):  
Bondan Bernadi ◽  
Yuni Budi Pramudyo ◽  
Fatima Omar Alawadhi ◽  
Alia Belal Zuwaid Belal Al Shamsi ◽  
Shamma Jasem Al Hammadi ◽  
...  
Keyword(s):  
History Matching ◽  
Material Balance ◽  
Static Model ◽  
Essential Elements ◽  
Gas Reservoirs ◽  
Gas Field ◽  
Uncertainty Range ◽  
Field Development ◽  
Static Modeling ◽  
Material Balance Method

Abstract FGIIP (Field Gas Initially in Place) is one of the most essential elements in building dependable static and Integrated Asset Model (IAM). A good estimation of FGIIP will improve history matching and generate reliable forecast. The mature gas field producing under depletion mode is an ideal example where P/Z technique can fit well to re-estimate the FGIIP. Even more, the estimation is also important to narrow down FGIIP uncertainties that initially existed in static model. Reliable FGIIP estimation is achieved by performing multiple P/Z calculations. The process involves dividing reservoir into key areas and each area is represented by individual P/Z prior to summing-up all P/Z to get the total FGIIP. Several scenarios are developed by defining key areas based on permeability variation, areal distribution and PVT behavior. The best FGIIP estimation is then fed back into the static model to generate numerous realizations considering the static uncertainties to produce the same FGIIP. Static models with realistic distribution of properties and good history match are used in the IAM model to generate forecast. The giant onshore gas field is highly heterogeneous having permeability, lateral composition variation and dynamic interaction between wells. To ensure that the heterogeneity observed in the field is honored, multiple key areas are defined by making areal sectorization and lateral PVT variation when estimating FGIIP with P/Z approach. Communication between areas was evidenced from the sectoral P/Z. The field history matching was improved after applying the new estimated FGIIP. It was observed that the sectoral history matching both for production and pressure matches from some selected realizations built in static model resulted in better matches. Succinctly the re-evaluation of static derived FGIIP with P/Z method for the mature gas field was able to reduce the uncertainty range that initially existed. Incorporating the correct estimation of FGIIP in IAM has helped to yield reliable forecast and has enabled the asset to plan proper work programs for further field development. Analytical material balance with P/Z is very applicable for mature gas reservoirs producing under depletion mode. The approach is worth doing to narrow down the uncertainty range that was previously calculated. Moreover, the integration of analytical P/Z with static and dynamic model (IAM) has achieved more reliable forecasting of the mature gas field to proceed with further development plan.

scholarly journals Variations in Permeability and Porosity of Synthetic Oil Reservoir Rock-Methods of Control

1965 ◽  
Vol 5 (04) ◽  
pp. 329-332 ◽  
Author(s):  
Larman J. Heath
Keyword(s):  
Porous Media ◽  
Relative Permeability ◽  
Reservoir Rock ◽  
Oil Reservoir ◽  
Natural Reservoir ◽  
Natural Rock ◽  
Unconsolidated Sand ◽  
Synthetic Rock ◽  
Porosity And Permeability ◽  
Relative Permeability Curves

Abstract Synthetic rock with predictable porosity and permeability bas been prepared from mixtures of sand, cement and water. Three series of mixes were investigated primarily for the relation between porosity and permeability for certain grain sizes and proportions. Synthetic rock prepared of 65 per cent large grains, 27 per cent small grains and 8 per cent Portland cement, gave measurable results ranging in porosity from 22.5 to 40 per cent and in permeability from 0.1 darcies to 6 darcies. This variation in porosity and permeability was caused by varying the amount of blending water. Drainage- cycle relative permeability characteristics of the synthetic rock were similar to those of natural reservoir rock. Introduction The fundamental behavior characteristics of fluids flowing through porous media have been described in the literature. Practical application of these flow characteristics to field conditions is too complicated except where assumptions are overly simplified. The use of dimensionally scaled models to simulate oil reservoirs has been described in the literature. These and other papers have presented the theoretical and experimental justification for model design. Others have presented elements of model construction and their operation. In most investigations the porous media have consisted of either unconsolidated sand, glass beads, broken glass or plastic-impregnated granular substances-materials in which the flow behavior is not identical to that in natural reservoir rock. The relative permeability curves for unconsolidated sands differ from those for consolidated sandstone. The effect of saturation history on relative permeability measurements A discussed by Geffen, et al. Wygal has shown quite conclusively that a process of artificial cementation can be used to render unconsolidated packs into synthetic sandstones having properties similar to those of natural rock. Many theoretical and experimental studies have been made in attempts to determine the structure and properties of unconsolidated sand, the most notable being by Naar and Wygal. Others have theorized and experimented with the fundamental characteristics of reservoir rocks. This study was conducted to determine if some general relationship could be established between the size of sand grains and the porosity and permeability in consolidated binary packs. This paper presents the results obtained by changing some of the factors which affect the porosity and permeability of synthetically prepared sandstone. In addition, drainage relative permeability curves are presented. EXPERIMENTAL PROCEDURE Mixtures of Portland cement with water and aggregate generally are designed to have certain characteristics, but essentially all are planned to be impervious to water or other liquids. Synthetic sandstone simulating oil reservoir rock, however, must be designed to have a given permeability (sometimes several darcies), a porosity which is primarily the effective porosity but quantitatively similar to natural rock, and other characteristics comparable to reservoir rock, such as wettability, pore geometry, tortuosity, etc. Unconsolidated ternary mixtures of spheres gave both a theoretically computed and an experimentally observed minimum porosity of about 25 per cent. By using a particle-distribution system, one-size particle packs had reproducible porosities in the reproducible range of 35 to 37 per cent. For model reservoir studies of the prototype system, a synthetic rock having a porosity of 25 per cent or less and a permeability of 2 darcies was required. The rock bad to be uniform and competent enough to handle. Synthetic sandstone cores mere prepared utilizing the technique developed by Wygal. Some tight variations in the procedure were incorporated. The sand was sieved through U.S. Standard sieves. SPEJ P. 329ˆ

Diagnostic Plots for Production Decline During the Transition of Oil Field Development From Depletion to Water Injection

2021 ◽  
Author(s):  
Vil Syrtlanov ◽  
Yury Golovatskiy ◽  
Ivan Ishimov
Keyword(s):  
Water Injection ◽  
Simulation Models ◽  
Oil Field ◽  
Production Data ◽  
Oil Reservoir ◽  
Field Development ◽  
Well Production ◽  
Diagnostic Plots ◽  
Oil Field Development ◽  
Analytical Approaches

Abstract In this paper the simplified way is proposed for predicting the dynamics of liquid production and estimating the parameters of the oil reservoir using diagnostic curves, which are a generalization of analytical approaches, partially compared with the results of calculations on 3D simulation models and with actual well production data.

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Simulation ◽  
History Matching ◽  
Hybrid Approach ◽  
Flow Simulation ◽  
Latin Hypercube Sampling ◽  
Simulation Models ◽  
Fine Scale ◽  
Simulation Results ◽  
Optimization Loop

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

Work Flow History Matches Numerical Simulation Models of Fractured Shale Wells

2021 ◽  
Vol 73 (04) ◽  
pp. 60-61
Author(s):  
Chris Carpenter
Keyword(s):  
Numerical Simulation ◽  
Fracture Toughness ◽  
Hydraulic Fracturing ◽  
History Matching ◽  
Simulation Models ◽  
Work Flow ◽  
Fracture Length ◽  
Matching Process ◽  
Rate Transient Analysis ◽  
Reservoir Simulator

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199149, “Rate-Transient-Analysis-Assisted History Matching With a Combined Hydraulic Fracturing and Reservoir Simulator,” by Garrett Fowler, SPE, and Mark McClure, SPE, ResFrac, and Jeff Allen, Recoil Resources, prepared for the 2020 SPE Latin American and Caribbean Petroleum Engineering Conference, originally scheduled to be held in Bogota, Colombia, 17–19 March. The paper has not been peer reviewed. This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. Sensitivity analysis simulations are performed with a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop what the authors term “motifs” that inform the history-matching process. Using intuition from these simulations, history matching can be expedited by changing matrix permeability, fracture conductivity, matrix-pressure-dependent permeability, boundary effects, and relative permeability. Introduction This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199149, “Rate-Transient-Analysis-Assisted History Matching With a Combined Hydraulic Fracturing and Reservoir Simulator,” by Garrett Fowler, SPE, and Mark McClure, SPE, ResFrac, and Jeff Allen, Recoil Resources, prepared for the 2020 SPE Latin American and Caribbean Petroleum Engineering Conference, originally scheduled to be held in Bogota, Colombia, 17-19 March. The paper has not been peer reviewed. This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. Sensitivity analysis simulations are performed with a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop what the authors term “motifs” that inform the history-matching process. Using intuition from these simulations, history matching can be expedited by changing matrix permeability, fracture conductivity, matrix-pressure-dependent permeability, boundary effects, and relative permeability. Introduction The concept of rate transient analysis (RTA) involves the use of rate and pressure trends of producing wells to estimate properties such as permeability and fracture surface area. While very useful, RTA is an analytical technique and has commensurate limitations. In the complete paper, different RTA motifs are generated using a simulator. Insights from these motif simulations are used to modify simulation parameters to expediate and inform the history- matching process. The simulation history-matching work flow presented includes the following steps: 1 - Set up a simulation model with geologic properties, wellbore and completion designs, and fracturing and production schedules 2 - Run an initial model 3 - Tune the fracture geometries (height and length) to heuristic data: microseismic, frac-hit data, distributed acoustic sensing, or other diagnostics 4 - Match instantaneous shut-in pressure (ISIP) and wellhead pressure (WHP) during injection 5 - Make RTA plots of the real and simulated production data 6 - Use the motifs presented in the paper to identify possible production mechanisms in the real data 7 - Adjust history-matching parameters in the simulation model based on the intuition gained from RTA of the real data 8 -Iterate Steps 5 through 7 to obtain a match in RTA trends 9 - Modify relative permeabilities as necessary to obtain correct oil, water, and gas proportions In this study, the authors used a commercial simulator that fully integrates hydraulic fracturing, wellbore, and reservoir simulation into a single modeling code. Matching Fracturing Data The complete paper focuses on matching production data, assisted by RTA, not specifically on the matching of fracturing data such as injection pressure and fracture geometry (Steps 3 and 4). Nevertheless, for completeness, these steps are very briefly summarized in this section. Effective fracture toughness is the most-important factor in determining fracture length. Field diagnostics suggest considerable variability in effective fracture toughness and fracture length. Typical half-lengths are between 500 and 2,000 ft. Laboratory-derived values of fracture toughness yield longer fractures (propagation of 2,000 ft or more from the wellbore). Significantly larger values of fracture toughness are needed to explain the shorter fracture length and higher net pressure values that are often observed. The authors use a scale- dependent fracture-toughness parameter to increase toughness as the fracture grows. This allows the simulator to match injection pressure data while simultaneously limiting fracture length. This scale-dependent toughness scaling parameter is the most-important parameter in determining fracture size.

scholarly journals METHODS OF OIL SATURATION MODELING AND THEIR APPLICATION IN IRAP RMS

2020 ◽  
pp. 69-78
Author(s):  
Елизавета Алексеевна Тихомирова
Keyword(s):  
Oil Content ◽  
Flow Simulation ◽  
Reservoir Modeling ◽  
Well Log ◽  
Oil Saturation ◽  
Geological Models ◽  
Modeling Software ◽  
Log Interpretation ◽  
Well Log Interpretation

Одним из важнейших свойств залежи является нефтенасыщенность. При моделировании распределение нефтенасыщенности является одним из основных параметров для подсчета запасов и дальнейшего гидродинамического моделирования. В статье рассмотрены варианты построения куба нефтенасыщенности с учетом априорной информации в виде данных результатов интерпретации геофизических исследований скважины, капилляриметрических испытаний и 3Д-трендов, а также алгоритмы реализации описанных методов в программной среде IRAP RMS. Построены геологические модели по описанным методам. Proper distribution of oil content is one of the main parameters for reserves assessment and further flow simulation. The paper reveals the variety of approaches to oil saturation cube building based on well log interpretation data, capillarimetry and 3D-trends and also the algorithms of the realization of these methods in reservoir modeling software IRAP RMS. Geological models are built using the described approaches.

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