Simultaneous History-Matching Approach by Use of Reservoir-Characterization and Reservoir-Simulation Studies

2016 ◽  
Vol 19 (04) ◽  
pp. 694-712 ◽  
Author(s):  
Guilherme Daniel Avansi ◽  
Célio Maschio ◽  
Denis José Schiozer
Keyword(s):  
Numerical Simulation ◽  
History Matching ◽  
Reservoir Characterization ◽  
Well Log ◽  
Log Data ◽  
Field Development ◽  
Geostatistical Methods ◽  
Well Production ◽  
Matching Process ◽  
Geological Models

Summary Reservoir characterization is the key to success in history matching and production forecasting. Thus, numerical simulation becomes a powerful tool to achieve a reliable model by quantifying the effect of uncertainties in field development and management planning, calibrating a model with history data, and forecasting field production. History matching is integrated into several areas, such as geology (geological characterization and petrophysical attributes), geophysics (4D-seismic data), statistical approaches (Bayesian theory and Markov field), and computer science (evolutionary algorithms). Although most integrated-history-matching studies use a unique objective function (OF), this is not enough. History matching by simultaneous calibrations of different OFs is necessary because all OFs must be within the acceptance range as well as maintain the consistency of generated geological models during reservoir characterization. The main goal of this work is to integrate history matching and reservoir characterization, applying a simultaneous calibration of different OFs in a history-matching procedure, and keeping the geological consistency in an adjustment approach to reliably forecast production. We also integrate virtual wells and geostatistical methods into the reservoir characterization to ensure realistic geomodels, avoiding the geological discontinuities, to match the reservoir numerical model. The proposed methodology comprises a geostatistical method to model the spatial reservoir-property distribution on the basis of the well-log data; numerical simulation; and adjusting conditional realizations (models) on the basis of geological modeling (variogram model, vertical-proportion curve, and regularized well-log data). In addition, reservoir uncertainties are included, simultaneously adjusting different OFs to evaluate the history-matching process and virtual wells to perturb geological continuities. This methodology effectively preserves the consistency of geological models during the history-matching process. We also simultaneously combine different OFs to calibrate and validate the models with well-production data. Reliable numerical and geological models are used in forecasting production under uncertainties to validate the integrated procedure.

Applications of Research and Development of Logging in Oil-Field Visualization

2013 ◽  
Vol 340 ◽  
pp. 867-870
Author(s):  
Quan Hou Li ◽  
Chun Yu Zhang ◽  
Yuan Feng Zhang
Keyword(s):  
Numerical Simulation ◽  
Research And Development ◽  
Traditional Method ◽  
Mathematical Theory ◽  
History Matching ◽  
Oil Field ◽  
Geological Modeling ◽  
Field Development ◽  
Oil Field Development ◽  
Geological Models

The research of visualization will be carried out in the aspects of parameters distribution, dynamic history matching and geological models and numerical simulation of reservoirs respectively. In terms of the research on reservoir parameters, we can improve the accuracy of the prediction through comparing, analyzing and modeling the data of old wells and the secondary explained data. As to dynamic history matching and numerical simulation, by combination of dynamic and statistic methods, we can modify the deficiency of the traditional method. As for geological modeling, we can utilize the characters of logging responses and current mathematical theory to establish modeling. Only by combination of the dynamic and static methods, we can achieve actual visualization of oil-field development.

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.

Managing Uncertainty of Well Log Data in Reservoir Characterization

2009 ◽  
Author(s):  
Paddy Deo ◽  
Dong Cynthia Xue ◽  
Freddy Mendez
Keyword(s):  
Reservoir Characterization ◽  
Well Log ◽  
Log Data

scholarly journals A Parallel Stochastic Framework for Reservoir Characterization and History Matching

2011 ◽  
Vol 2011 ◽  
pp. 1-19 ◽  
Author(s):  
Sunil G. Thomas ◽  
Hector M. Klie ◽  
Adolfo A. Rodriguez ◽  
Mary F. Wheeler
Keyword(s):  
Porous Media ◽  
Spatial Distribution ◽  
Multiphase Flow ◽  
History Matching ◽  
Reservoir Characterization ◽  
Well Log ◽  
Species Transport ◽  
Medium Permeability ◽  
Stochastic Framework ◽  
Sensor Information

The spatial distribution of parameters that characterize the subsurface is never known to any reasonable level of accuracy required to solve the governing PDEs of multiphase flow or species transport through porous media. This paper presents a numerically cheap, yet efficient, accurate and parallel framework to estimate reservoir parameters, for example, medium permeability, using sensor information from measurements of the solution variables such as phase pressures, phase concentrations, fluxes, and seismic and well log data. Numerical results are presented to demonstrate the method.

scholarly journals Hydrocarbon reservoir characterization of “Otan-Ile” field, Niger Delta

2021 ◽  
Vol 11 (2) ◽  
pp. 601-615
Author(s):  
Tokunbo Sanmi Fagbemigun ◽  
Michael Ayu Ayuk ◽  
Olufemi Enitan Oyanameh ◽  
Opeyemi Joshua Akinrinade ◽  
Joel Olayide Amosun ◽  
...  
Keyword(s):  
Niger Delta ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Structural Deformation ◽  
Well Log ◽  
Reservoir Properties ◽  
Good Tool ◽  
Log Data ◽  
Hydrocarbon Reservoir ◽  
Hydrocarbon Saturation

AbstractOtan-Ile field, located in the transition zone Niger Delta, is characterized by complex structural deformation and faulting which lead to high uncertainties of reservoir properties. These high uncertainties greatly affect the exploration and development of the Otan-Ile field, and thus require proper characterization. Reservoir characterization requires integration of different data such as seismic and well log data, which are used to develop proper reservoir model. Therefore, the objective of this study is to characterize the reservoir sand bodies across the Otan-Ile field and to evaluate the petrophysical parameters using 3-dimension seismic and well log data from four wells. Reservoir sands were delineated using combination of resistivity and gamma ray logs. The estimation of reservoir properties, such as gross thickness, net thickness, volume of shale, porosity, water saturation and hydrocarbon saturation, were done using standard equations. Two horizons (T and U) as well as major and minor faults were mapped across the ‘Otan-Ile’ field. The results show that the average net thickness, volume of shale, porosity, hydrocarbon saturation and permeability across the field are 28.19 m, 15%, 37%, 71% and 26,740.24 md respectively. Two major faults (F1 and F5) dipping in northeastern and northwestern direction were identified. The horizons were characterized by structural closures which can accommodate hydrocarbon were identified. Amplitude maps superimposed on depth-structure map also validate the hydrocarbon potential of the closures on it. This study shows that the integration of 3D seismic and well log data with seismic attribute is a good tool for proper hydrocarbon reservoir characterization.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

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