In order to design and analyse Alkaline Surfactant Polymer (ASP) pilots and generate reliable field forecasts, a robust scalable modeling workflow for the ASP process is required. Accurate modeling of an ASP flood requires detailed representation of the geochemistry and the saponification process, if natural acids are present. The objective of this study is to extend the existing models of ion exchange and surfactant partitioning between phases to improve the quality of the model.
Geochemistry and saponification affect the propagation of the injected chemicals. This in turn determine the chemical phase behaviour and hence the effectiveness of the ASP process. A starting point of such a workflow is to carry out ASP coreflood tests and history matching (HM) using numerical models. This allows validation of the models and generates a set of chemical flood parameters that can be used for forecasts. The next step is upscaling from lab to field. The presence of (geo)-chemistry in ASP model improves significantly the quality of core HM especially for produced chemicals, breakthrough time and their profiles shape.
The addition of surfactant partitioning between the oleic and the aqueous phases based on salinity of the system as well as propagated distance (time) improves understanding of the required surfactant concentration. The partitioning of surfactant is important for coreflood matching of native cores as they tend to have more clays and minerals that affect ASP phase behaviour. The upscaling of the HM coreflood was conducted in two steps. First step the coreflood was scaled up with the distance between injector–producer pair as the scaling parameter. Second step was the application of the scaled up injection rates, residual saturations, etc. to the full field model. Sensitivity study for parameters such as grid size, well distance, ASP slug size, and rate of surfactant partitioning was performed. It was found that grid size of 50ft was optimum for ASP modeling. The higher rate of surfactant partitioning resulted to lower recovery. The optimal well distance was determined as 700ft for optimization of oil recovery. The reduction of ASP slug size from 0.5PV to 0.3PV leads to the reduction in oil recovery by 2-3%.
Usually chemical reactions accompanied ASP process are left out of the model due to increase in complexity as well as longer computational time. However, their addition as well as presence of surfactant partitioning between the oleic and the aqueous phases makes ASP models more realistic and it results in significant improvement to coreflood HM quality and prediction of ASP process.
Production data analysis is the key to provide pertinent reservoir information in-terms of reservoir container volume, depletion mechanism, reservoir connectivity and well performances. This study focused on the analytical methods such as Rate Transient Analysis (RTA), Flowing Material Balance (FMB), Pressure Transient Analysis (PTA) and analytical simulation as an integrated approach toward enhanced production data analysis in the oil fields. The idea of FMB has been introduced by
In this paper, the above mentioned methods and workflow is elaborated and a case study using this integrated approach is discussed for better understanding of the methodology.
Integrated reservoir modeling with representative data is crucial for an effective reservoir management and depletion plan. Both analytical and numerical approaches benefit from this integrated process. The objective of this study is to incorporate the outcomes of analytical techniques such as rate transient analysis (RTA) and pressure transient analysis (PTA) into numerical reservoir model to have a better understanding of drive mechanisms, reservoir connectivity with minimal time-consuming for history matching efforts but a more reliable production forecast.
In order to demonstrate the methodology, a clastic reservoir from Malay basin was considered. Sedimentology and sequence stratigraphy studies were performed to have a better picture of heterogeneity and zonation of the reservoir. All production and injection data were investigated along with pressure data to filter data inconsistency. Shut-in time should be long enough to take representative reservoir pressure and accordingly material balance study conducted for accessible volume for a given area. However, flowing material balance is able to be applied with no restriction on the production data for evaluation of historical data and prediction cases.
The boundary of the channel sand was constructed based on the well log data and seismic attributes. Amplitude impedance was used as a guide for lithofacies and porosity distribution in the geological model. In addition, stratigraphy definition with further details were incorporated. Lithofacies, petrophysical and SCAL data were incorporated in rock-type classification and accordingly saturation-height-function were modelled. Analytical approaches including PTA, material balance, and RTA were utilized to have a better understanding of fluid flow and drive mechanisms. The well and reservoir properties and also connected volume from analytical approaches were utilized as a tuning tool of static model. This approach considerably reduced the iteration between static and dynamic models for history matching exercise. Afterwards, the production forecast were conducted with two development opportunities identified.
In this study, an integrated methodology was applied to mitigate the complexity of history matching task. Moreover, it is demonstrated that using such analytical methods help to improve the development plan of a given field significantly.
With the increasing demand in domestic energy requirement and with declining production rates from mature fields of offshore Malaysia, PETRONAS has embarked on an aggressive campaign to address the decline in rates as well as increase the reserves through proven Enhanced Oil Recovery (EOR) application. An immiscible Water Alternating Gas (WAG) process is found to be the most favorable EOR method due to gas supply availability, proven world-wide application, and promising results in improving injection fluid sweep efficiency and reducing residual oil saturation.
To reduce the uncertainty of EOR technical studies under low oil price, a comprehensive integrated procedure is required to study WAG performance and define key factors that impact flow efficiency under three-phase flow conditions for a more representative full-field reservoir simulation study results.
This procedure involves a detailed comprehensive parametric study of the cycle dependent hysteresis starting from extensive literature review, followed by laboratory experiments and extracting pertinent WAG parameters from coreflood history matching and finally applying these parameters in full-field reservoir simulation study. This study demonstrated that the WAG cycle dependency of relative permeability during WAG process is one of the key factors that has significant impact on WAG performance and recovery factor. This feature cannot be captured by conventional three-phase flow models used by reservoir simulators. The study indicates additional recovery factor of about 1%-2% compared to the base-case WAG model without WAG hysteresis.
Chemical flooding is one of the challenging EOR methods to improve the oil recovery. The objective of this work is to examine a systematic approach for upscaling Alkaline Surfactant Polymer (ASP) coreflood data to field scale and design the single well chemical tracer (SWCT) test. Appropriate upscaling can help to determine the effect of crucial parameters on process mechanisms and oil recovery. Besides, uncertainty assessment should be conducted thoroughly to evaluate the impact of key parameters.
In this paper, a robust approach for modeling the ASP flood from core to reservoir scale including the data uncertainty would be presented. Experimental work was aimed to screen and select the suitable chemicals for implementation in the field. Coreflood tests include ASP flood with and without polymer chase with the objective to evaluate the effectiveness of chemical flood and sweep efficiency. Sensitivity analysis by response surface methodology (RSM) would help to find the crucial parameters during the history matching of coreflood tests and reservoir modeling for ASP implementation.
Coreflood modeling was performed to represent the flow behavior of lab tests and investigate the mechanisms through the experimental efforts. Assisted history matching of the coreflood test was carried out to incorporate waterflood and chemical flood processes. Some variables such as relative permeability characteristics, trapping number, adsorption, and residual resistance factor were included as matching parameters. The next step was to upscale the model from core scale to reservoir scale by an appropriate method. Velocity and pressure were preserved during the scaling procedure. The parameters obtained from scaling exercise were used for SWCT design and full field model. Thus, radial models were used to describe and improve the design of SWCT tests for candidate wells. The next step was to evaluate the ASP flood on reservoir model. Sensitivity analysis was conducted on key parameters e.g. adsorption, injector-producer spacing, residual oil reduction by chemical, and ASP slug size to identify the impact of these parameters on oil recovery. RSM was applied to develop a suitable proxy model based on the results of sensitivity study. The proxy model can be used to find the optimum well spacing and slug size for field implementation.
Appropriate technique of chemical flood modeling is presented in this work. Moreover, upscaling of lab data to reservoir scale for pilot design and evaluation of ASP flood on reservoir scale by considering how to address risks and uncertainties are other outcomes of this work.
Das, Apurba Kumar (PETRONAS Carigali Sdn. Bhd.) | Arsanti, Dian (PETRONAS Carigali Sdn. Bhd.) | Ghadami, Nader (PETRONAS Carigali Sdn. Bhd.) | Tunio, Kamran Haider (PETRONAS Carigali Sdn. Bhd.) | Dou Ali, Asem Mahmoud A (PETRONAS Carigali Sdn. Bhd.)
An integrated modeling approach was used in a thin, highly heterogeneous complex clastic reservoir to build a more robust three-dimensional geo-cellular model by incorporating all available data from geoscience disciplines such as geophysics, geology, petrophysics and reservoir engineering. The model was used to replicate actual reservoir flow dynamic and generate optimum development strategy.
The early versions of the model relied heavily on seismic attributes to populate the reservoir facies. The poor seismic resolution of this thin reservoir has resulted in a patchy facies distribution which could not represent the actual fluid behavior during dynamic modeling. An alternative modeling approach was followed which populates the sand based on net sand mapping which honors the conceptual depositional model of this reservoir. As a result, this approach was used to improve the static model based on actual observed data like pressure and injection response in nearby producers and performance of well behavior.
A more reliable reservoir model was built after a lot of iterations between static and dynamic models without changing basic conceptual geology. The characterization of the reservoir as fluvial/deltaic mouth bar lobes has helped to capture a realistic heterogeneity level of the reservoir which made the history matching process much easier. Critically well-wise production performance analysis was conducted before proceeding to dynamic modeling. Production allocation anomaly was observed in few wells and necessary weight has been given to these wells during history matching. One of the key parameters that needs high attention particularly for reservoirs under waterflooding is relative permeability. Because of lack of availability and high uncertainty of relative permeability data in this reservoir, a systematic sensitivity analysis was performed on end point relative permeability and Corey parameters. As a result, the impact of each examined parameter on reservoir performance was scrutinized. The concept of producer injector pairing was used during history matching process and satisfactory history match achieved with minimum property modifiers.
A real case example was used to introduce an alternative modeling approach for thin, below seismic resolution clastic reservoirs to achieve a satisfactory history matched model. The presented approach would provide rigorous and sound reservoir models. The sensitivity analysis on relative permeability parameters expedites to the better understanding of flow behavior of the reservoir.
Chemical EOR is one of the promising methods to improve the oil recovery. However, due to high cost of the process, there are challenges to minimize the cost and maximize the oil recovery. Some influencing parameters should be taken into account in a systematic approach to find their impact on oil recovery and accordingly optimizing the process.
In this study, we present a robust optimization workflow of alkaline-surfactant (AS) flooding into a thin clastic reservoir of a field in the Malay Basin. There are coreflood experiments and pilot tests on this field that can be quite helpful to provide a basis to find out the appropriate range of input parameters. Optimization work is based on response surface methodology (RSM) and particle swarm optimization (PSO) technique that aid us to indicate the optimum oil recovery from chemical flooding. In order to get the utmost advantage of this workflow, the waterflooding should be optimized prior to the chemical flooding optimization to maximize the sweep efficiency and oil recovery from the chemical flood.
Evaluation of coreflood and pilot tests indicated that some parameters need supplementary evaluation to investigate their effect on reservoir performance and flow dynamics. These parameters include residual oil reduction by chemical, relative permeability curves, chemicals adsorption, chemical concentration, slug size, injection rate, and initiation time of chemical injection. Based on the result of tornado chart, residual oil reduction and injection rate exhibited highest and lowest impact on oil recovery. RSM was used to explore the relationship between input variables and objective function. Some design parameters such as chemical concentration, slug size and initiation time were examined in this stage. Afterwards, proxy models have been built using polynomial regression and neural network methods. The results showed that the proxy model by neural network method revealed better performance for prediction of the simulation results. The proxy model was used to calculate the oil recovery for any combination of input parameters. Besides, it was used to assess the parameter sensitivity and identify the impact of any input parameter on oil recovery. At the next stage, PSO method was utilized to optimize the oil recovery by chemical flooding. It was found that the optimized water injection rate and pattern for water flooding scenario need further optimization to improve the sweep efficiency and thereby oil recovery by AS flooding at later stage. Running numerous simulation cases is normally expected to optimize the process by conventional methods and the proposed PSO approach can be used to reduce the number of runs significantly. Sensitivity analysis provided a very good understanding about reserve ranges for the different influential parameters. Optimizing the cost of chemical flooding and improving oil recovery are other outcomes of this study.
Chemical flooding is one of the enhanced oil recovery (EOR) methods to mobilize the residual oil after waterflooding by reduction of oil-water interfacial tension or wettability alteration and consequently increase of capillary number. The objective of this study is to assess the potential of alkaline surfactant (AS) flooding on a major oil reservoir at offshore Malaysia, besides to conduct the uncertainty assessment. High capital and operating expenditure are associated with chemical EOR (CEOR) projects. Therefore, key subsurface uncertainties quantified thoroughly for a robust assessment of influential parameters.
The considered field is planned to be a pioneer in offshore CEOR. After the completion of pilot and matching the results in the simulation model, the key step is to upscale the results to the full field level. A critical step of the chemical EOR is to find the relative contribution of the influential parameters on the objective function like oil recovery. To do so, a detailed modeling work was performed for sensitivity and uncertainty analysis of AS flooding. Some of the important parameters used in the model are interfacial tension, chemical adsorption, slug size, and reduction of residual oil saturation by chemical.
A single well pilot test project was successfully conducted in 2007. The pilot entailed the injection of Alkaline-Surfactant chemicals and chemical tracer test into a waterflooded reservoir and produced from the same well. The pilot test indicated significant reduction of residual oil saturation (Sorw) in the range of 50% to 80% of Sorw. After running the uncertainty cases for the targeted reservoir, the probability ranges of the objective function were established. Based on the results of this uncertainty analysis, a proxy model has been built and subsequently quality checked to ensure that it can reproduce the simulated data with high accuracy. The developed proxy model was used to capture all combination of parameter ranges and do better decision making on the project. The results showed that based on the corresponding ranges of parameters, the residual oil reduction and slug size exhibited the highest and lowest impact on oil recovery, respectively. Therefore, the uncertainty of the objective function can be reduced by mitigating the uncertainty of the most influential parameters. Moreover, this work presents a proper workflow of CEOR modeling in addition to detailed and systematic approach for uncertainty evaluation.