Development of Integrated Capacitance Resistive Model for predicting waterflood performance: a study on formation damage

Salehian, Mohammad; Soleimani, Rasa

doi:10.1080/15567036.2018.1486922

Cited by 5 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shut-ins especially results in re-allocation of streamlines between the injector and producer, which leads to a difficulty in determining the injection weights for CRM [11]. Recently, Salehian and Soleimani [12] developed a comprehensive mathematical formula for CRM to predict waterflood performance and match the history of either vertical or horizontal wells including shut-in periods. There are several versions of CRM in the literature.…”

Section: Reservoir Characterization Using Capacitance-resistance Modelmentioning

confidence: 99%

“…Kim [13] presented a linear multivariate regression analysis (ICRM) which fits the cumulative total production with cumulative water injections. Salehian and Soleimani [12] improved the accuracy of ICRM by utilizing two consecutive objective functions. Salehian [11] developed a dynamic CRM (D-CRMP) to address the issue of CRMs with shut-in periods in historical data.…”

Section: Reservoir Characterization Using Capacitance-resistance Modelmentioning

confidence: 99%

See 1 more Smart Citation

Reservoir Management Through Characterization of Smart Fields Using Capacitance-Resistance Models

Salehian

Temizel²,

Gok

et al. 2018

Day 3 Wed, November 14, 2018

Self Cite

View full text Add to dashboard Cite

Use of smart well technologies to improve the recovery has caught significant attention in the oil industry in the last decade. Capacitance-Resistance (CRM) methodology is a robust data-driven technique for reservoir surveillance. Reservoir sweep is a crucial part of efficient recovery, especially where significant investment is done by means of installation of smart wells that feature inflow control valves (ICVs) that are remotely controllable. However, as it is a relatively newer concept, effective use of this new technology has been a challenge. In this study, the objective is to present the efficient use of ICVs in intelligent fields through the integrated use of capacitance-resistance modeling and smart wells with ICVs. A standard realistic SPE reservoir simulation model of a waterflooding process is used in this study where the smart well ICVs are controlled with conditional statements called procedures in a fully commercial full-physics numerical reservoir simulator. The simulation data is utilized to build the CRM model to obtain the inter-well connectivities at the zonal level beyond only the inter-well connectivity data as smart wells provide control and information on the amount of injection into each layer or zone. Thus, after analyzing the CRM model to detect the inter-well connectivities at the zone/layer-level in an iterative way, the optimum injection not only at the well level but also at the perf/zone level is found. The workflow is outlined as well as the improvements in the results. The smart well technology has been challenged with the associated cost component thus, it is important to present the benefits of this technology with applications in more diverse cases with different workflows. It has been observed that a robust reservoir characterization in an intelligent field can provide an insight into the physics of reservoir including smart wells with ICVs. The results are presented in a comparative way against the base case to illustrate the incremental value of the use of ICVs along with key performance indicators. Most importantly, it has been shown that smart well use without a robust reservoir management strategy does not always lead to successful results. In reservoir management, it is not only important to catch the well level details but also see the big picture at the field level to improve the performance of the reservoirs beyond individual well performances taking into account the interference between wells. This method takes the reservoir surveillance to the next level where reservoir characterization is improved using smart field technologies and capacitance-resistance modeling as a robust cost-effective data-driven method.

show abstract

Section: Reservoir Characterization Using Capacitance-resistance Modelmentioning

confidence: 99%

Section: Reservoir Characterization Using Capacitance-resistance Modelmentioning

confidence: 99%

Reservoir Management Through Characterization of Smart Fields Using Capacitance-Resistance Models

Salehian

Temizel²,

Gok

et al. 2018

Day 3 Wed, November 14, 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…This work focuses on the CO 2 storage process (no production stage) using a simplified version of CRM fitting the objective. Also, the previous works mainly investigated the theoretical aspects of capacitance resistance-based models and comparison of the CRM results with the synthetic simulation results (Bastami et al 2012b;Salehian and Soleimani 2018;Sayarpour et al 2009b). However, capacitance resistance-based models to evaluate reservoir performance using field data were presented in limited studies mainly for waterflooding (Mamghaderi et al 2013;Nguyen et al 2011aNguyen et al , 2011bSayarpour et al 2009a;Temizel et al 2018Wang et al 2011.…”

Section: Introductionmentioning

confidence: 99%

Application of a physics-based lumped parameter model to evaluate reservoir parameters during CO2 storage

Raziperchikolaee

Mishra

2020

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Evaluating reservoir performance could be challenging, especially when available data are only limited to pressures and rates from oil field production and/or injection wells. Numerical simulation is a typical approach to estimate reservoir properties using the history match process by reconciling field observations and model predictions. Performing numerical simulations can be computationally expensive by considering a large number of grids required to capture the spatial variation in geological properties, detailed structural complexity of the reservoir, and numerical time steps to cover different periods of oil recovery. In this work, a simplified physics-based model is used to estimate specific reservoir parameters during CO2 storage into a depleted oil reservoir. The governing equation is based on the integrated capacitance resistance model algorithm. A multivariate linear regression method is used for estimating reservoir parameters (injectivity index and compressibility). Synthetic scenarios were generated using a multiphase flow numerical simulator. Then, the results of the simplified physics-based model in terms of the estimated fluid compressibility were compared against the simulation results. CO2 injection data including bottom hole pressure and injection rate were also gathered from a depleted oil reef in Michigan Basin. A field application of the simplified physics-based model was presented to estimate above-mentioned parameters for the case of CO2 storage in a depleted oil reservoir in Michigan Basin. The results of this work show that this simple lumped parameter model can be used for a quick estimation of the specific reservoir parameters and its changes over the CO2 injection period.

show abstract

Reservoir characterization using dynamic capacitance–resistance model with application to shut-in and horizontal wells

Salehian

Çınar

2019

J Petrol Explor Prod Technol

Self Cite

View full text Add to dashboard Cite

Capacitance-resistance model (CRM) is a nonlinear signal processing approach that provides information about interwell communication and reservoir heterogeneity. Several forms of CRM have been introduced; however, they would deliver erroneous model parameters if production history involves shut-in period. To address this issue, this study presents a dynamic capacitance-resistance model (D-CRMP), a comprehensive formulation that is capable of handling multiple shut-in periods in different producers. CRM model parameters are representative of the geological information. Accordingly, two geologically identical synthetic examples are used to validate D-CRMP; one including shut-in periods in historical production data of some producers and the other one with all continuously operating wells. Obtaining the same model parameters and the high quality of fitting in both cases proved the reliability of D-CRMP, which allows the utilization of historical data to characterize the reservoir behavior in real cases. Investigation of uncertainty on the fitted model parameters was also performed to demonstrate that confidence intervals are affected mostly by two aspects; permeability distribution and interwell distance. It is shown that though the confidence intervals in the heterogeneous fields are relatively higher than the homogeneous examples, higher permeability and lower producer-injector distance reduce the uncertainty of model parameters in both cases. This study also applies the proposed model in reservoirs with horizontal wells and further examines the impact of well direction and length of the productive interval on the connectivities between wells. Keywords Capacitance-resistance model • Reservoir characterization • History matching • Waterflood • Shut-in well • Horizontal well Abbreviations BHP Bottom-hole pressure CMG Computer modeling group Ltd. CRM Capacitance-resistance model CWI Cumulative water injection D-CRMP Dynamic capacitance-resistance model ICRM Integrated capacitance-resistance model IMEX Implicit-explicit black oil simulator IWC Interwell connectivity MLR Multivariate linear regression MSHW Multi-segmented horizontal well List of symbols L, F, t Mean length, force, and time, respectively c t Total compressibility (L 2 /F) f ij Interwell connectivity between injector i and producer j , dimensionless I Water injection rate (L 3 /t) J Productivity index (L 5 /F − t) N p Cumulative liquid production rate (L 3 /t) n P Total number of producers n T Total number of historic time periods n I Total number of injectors P wf Well bottom-hole pressure (F/L 2) q Total liquid production rate (L 3 /t) R 2 Correlation coefficient Time constant, t

show abstract

Development of Integrated Capacitance Resistive Model for predicting waterflood performance: a study on formation damage

Cited by 5 publications

References 1 publication

Reservoir Management Through Characterization of Smart Fields Using Capacitance-Resistance Models

Reservoir Management Through Characterization of Smart Fields Using Capacitance-Resistance Models

Application of a physics-based lumped parameter model to evaluate reservoir parameters during CO2 storage

Reservoir characterization using dynamic capacitance–resistance model with application to shut-in and horizontal wells

Contact Info

Product

Resources

About