The field-scale design of chemical enhanced oil recovery (cEOR) processes requires running complex numerical models that are computationally demanding. This paper provides an efficient screening platform for the cEOR feasibility study by presenting five artificial neural network (ANN) based models. We constructed 1,100 ANN training cases using CMG-STARS to capture the variation in reservoir petrophysical properties and the range of injected chemicals properties for a five-spot pattern. The design parameters were coupled with the reservoir properties using several functional links to optimize the ANN models and improve their performances. The training cases were employed using back-propagation methods to construct one forward model (Model #1) and four inverse models. Model #1 predicts reservoir response (i.e., oil rate, water cut, injector bottomhole pressure, cumulative oil) for known reservoir characteristics (i.e., permeability, thickness, residual oil saturation, chemical adsorption) and project design parameters (i.e., pattern size, chemical slug size and concentration), Model #2 predicts reservoir characteristics by history matching the reservoir response, and Model #3 predicts project design parameters for known reservoir response and characteristics. Models #4 and #5 predict project design parameters for a targeted cumulative oil volume and project duration time, which is useful for economical evaluation before the implementation of cEOR projects.
The validation results show that the developed ANN-based models closely predict the numerical results. In addition, the models are able to reduce the computational time by four orders of magnitude, which is significant considering the complexity of cEOR modeling and the need for reliable and efficient tools in building cEOR feasibility studies. In terms of accuracy, Model #1 has a prediction error of 5% whereas the error for other four inverse ANN models is about 20–40%. To enhance the performance of the inverse ANN models, we changed the ANN structure, increased training cases, and used functional links, which slightly reduced the error. Further, we introduced a back-check loop that uses the predicted parameters from the inverse ANN models as inputs in the forward ANN model. A comparison of back-check results for the reservoir response with the numerical results delivers a relatively small error of 10%, revealing the non-uniqueness of solutions obtained from the inverse ANN models.
Aker Solutions and FSubsea have agreed to a joint venture, named FASTSubsea, to help operators increase oil recovery. Researchers from Chevron are looking into a new approach to understand the drivers of polymer hydration. How might this affect the design of mixing systems in the field, and could it affect offshore EOR applications? A pilot project demonstrates that facilities design plays an important role in providing sources of CO2 for the gas-handling process for injection into a carbonate formation as a tertiary recovery mechanism. The paper discusses the main factors affecting CO2 corrosion, provides an assessment of what to look for in major equipment, and details recommended material of construction and corrosion mitigation/control methods.
Reservoirs which produce under active water drive offer a significant uncertainty towards implementation of Chemical EOR processes. This paper describes a successful pilot testing of ASP process in a clastic reservoir which is operating under strong aquifer drive. The field has ~ 30 years of production history. The objective of the pilot was to understand response of ASP process in a mature reservoir, which is operating under active edge water drive. The build-up permeability of the reservoir is 2-8 Darcy with viscosity~ 50 cP. Salient key observations like production performance, incremental oil gain, polymer breakthrough etc. are discussed in this paper after completion of the pilot.
On the basis of laboratory study and simulation, ASP pilot was implemented in the field in 2010.The pilot was designed with single inverted five spot pattern and one observation well. The pilot envisaged injection of 0.3 pore volume (PV) Alkali-Surfactant-Polymer (ASP) slug, 0.3 PV graded polymer buffer followed by 0.4PV chase water. The pilot was meticulously monitored for production performance and breakthrough of chemicals. All the pilot producers have more than 20 years of production history. Base oil rate and water cut were fixed before start of the pilot, on the basis of test data which was used to monitor pilot performance. Interwell Tracer Test (IWTT) was conducted before starting of ASP injection so as to understand sweep in the pilot area. In addition, quality of injection water and chemical concentration in ASP slug was checked regularly to ensure best quality.
Significant response of the pilot was observed within 15 months of the start of the pilot which was published in 2012. This paper aims to describe the learning and conclusion after successful completion of the pilot. ~40-50% jump in oil rate was observed during the ASP injection period which sustained for 12-18 months. However preferential breakthrough of ASP slug in one of the producer impacted the incremental oil gain. The preferential breakthrough of polymer was due to presence of high permeability streaks which was rectified by profile modification job. In addition, strong aquifer movement was experienced during ASP injection which leads to rise in water cut of a pilot well. However, the pilot well was restored through water shutoff jobs. After completion of ASP and mobility buffer, a cumulative incremental oil ~28000 m3 was obtained. Cumulative incremental oil gain is in line with simulation studies prediction. 12-14% decrease in water cut was observed which sustained for ~ 6-18 months. Regular monitoring of produced fluid indicated breakthrough of polymer and alkali in 2-3 producers. During the pilot, produced fluid handling issues like tough emulsion formation, lift malfunctioning etc. was not observed. These collective observation indicated success of the ASP pilot project.
There are very few case histories of successful ASP pilot implementation are available, in which the reservoirs has been operating under active aquifer drive. Learning of this ASP project can be taken forward for expansion of ASP flood and also designing of ASP pilot/commercial projects for analogous reservoirs.
Mogollón, J. L. (Halliburton) | Yomdo, S. (OIL India Limited) | Salazar, A. (Halliburton) | Dutta, R. (OIL India Limited) | Bobula, D. (Halliburton) | Dhodapkar, P. K. (OIL India Limited) | Lokandwala, T. (Halliburton) | Chandrasekar, V. (CMG)
The perception of better economics and less risk from infill drilling and recompletions are reasons well-focused remedies are preferred compared to reservoir-focused solutions, such as enhanced oil recovery (EOR). However, most literature does not discuss the economic and risk indicators driving this.
Using a real example, this work demonstrates that combining polymer flooding with infill drilling and recompletion substantially increases economic benefits with reasonable risk.
The reservoir considered is an Oligocene sandstone at a depth of 2700 m. The °API is 29.5 and permeability ranges from 50 to 500 mD. Current reservoir pressure is 43% of the original and it is below bubble point. A black oil model with a 133 × 56 × 128 grid was used. The model incorporated more than 50 years of matched primary and waterflooding production history and experimental polymer physico-chemical parameters. For the stochastic economic risks estimation, 1,000 iterations were run for each scenario considering uncertainties in injection-production, capital expenditures (CAPEX), operational expenditures (OPEX), and oil prices.
For a 20-year horizon, the injection-production-pressure profiles were numerically forecasted; economic results were calculated using a classic model and inputs from the forecast. The economic risk was determined stochastically. The redevelopment scenarios considered were as follows: Base: current waterflooding Existing wells interventions: workover, opening shut-in wells, and new perforations Infill drilling: vertical/horizontal infill drilling wells + existing wells operations Polymer flooding: using existing wells Combined Infill and polymer: vertical infill drilling wells and polymer flooding
Base: current waterflooding
Existing wells interventions: workover, opening shut-in wells, and new perforations
Infill drilling: vertical/horizontal infill drilling wells + existing wells operations
Polymer flooding: using existing wells
Combined Infill and polymer: vertical infill drilling wells and polymer flooding
P50 forecasts showed that interventions in existing wells in the base scenario increased oil production by 11% and net present value (NPV) by 71% with a risk index of 0.38.
A numerical optimizer was used to account for possible combinations of 14 potential drilling locations and vertical to horizontal well ratios. A scenario with three vertical wells was selected. Compared to the base case, this scenario showed an oil production increase of 23%, NPV increase of 178%, and a risk index of 0.41.
The injection rate of the polymer flood was optimized, resulting in a 17% increase in oil production and 95% increase in the NPV, with a risk index of 0.40. This justifies performing a polymer flood.
The most promising scenario is the combined infill drilling and polymer injection, which significantly improved the economic indicators—30% increase in oil production, 230% improvement of the NPV over the base scenario, with a risk index of only 0.41.
The results of this study demonstrate that the combination of EOR with different operational strategies results in significant benefits compared to the individual scenarios. Analysis of just oil production independent of economics and risk can be misleading. Infill drilling or flooding should no longer be the question. Instead, the question should be how they can be properly combined at various stages of asset life.
Agrawal, Nitesh (Cairn Oil & Gas, Vedanta Limited) | Chapman, Tom (Cairn Oil & Gas, Vedanta Limited) | Baid, Rahul (Cairn Oil & Gas, Vedanta Limited) | Singh, Ritesh Kumar (Cairn Oil & Gas, Vedanta Limited) | Shrivastava, Sahil (Cairn Oil & Gas, Vedanta Limited) | Kushwaha, Malay Kumar (Cairn Oil & Gas, Vedanta Limited) | Kolay, Jayabrata (Cairn Oil & Gas, Vedanta Limited) | Ghosh, Priyam (Cairn Oil & Gas, Vedanta Limited) | Das, Joyjit (Cairn Oil & Gas, Vedanta Limited) | Khare, Sameer (Cairn Oil & Gas, Vedanta Limited) | Kumar, Piyush (Cairn Oil & Gas, Vedanta Limited) | Aggarwal, Shubham (Cairn Oil & Gas, Vedanta Limited)
The objective of this paper is to present a suite of diagnostic methods and tools which have been developed to analyse and understand production performance degredation in wells lifted by ESPs in the Mangala field in Rajasthan, India. The Mangala field is one of the world’s largest full field polymer floods, currently injecting some 450kbbl/day of polymerized water, and a significant proportion of production is lifted with ESPs. With polymer breaking through to the producers, productivity and ESP performance in many wells have changed dramatically. We have observed rapidly reducing well productivity indexes (PI), changes to the pumps head/rate curve, increased inlet gas volume fraction (GVF) and reduction in the cooling efficiency of ESP motors from wellbore fluids. The main drivers for the work were to understand whether reduced well rates were a result of reduced PI or a degredation in the ESP pump curve, and whether these are purely down to polymer or combined with other factors, for example reduced reservoir pressure, increasing inlet gas, scale buildup, mechanical wear or pump recirculation.
The methodology adopted for diagnosis was broken in 5 parts – 1) Real time ESP parameter alarm system, 2) Time lapse analysis of production tubing pressure drop, 3) Time lapse analysis of pump head de-rating factor, 4) Time lapse analysis of pump and VFD horse power 5) Dead head and multi choke test data. With this workflow we were able to break down our understanding of production loss into its constituent components, namely well productivitiy, pump head/rate loss or additional tubing pressure drop. It was also possible to further make a data driven asseesment as to the most likely mechanisms leading to ESP head loss (and therefore rate loss), to be further broken own into whether this was due to polymer plugging, mechanical wear, gas volume fraction (GVF) de-rating, partial broken shaft/locked diffusers or holes/recirculation. In some cases a specific mechanism was compounded with an associated impact. For example, in ESPs equipped with an inlet screen, heavy polymer deposition over the screen was resulting in large pressure drops across the screen leading to lower head, but this also resulted in higher GVFs into first few stages of the pump, even though the GVF outside the pump were low, leading to further head loss from gas de-rating of the head curve. With knowledge of the magnitude of production losses from each of the underlying mechanisms, targeted remediation could then be planned.
The well and pump modelling adopted in the workflow utilise standard industry calculations, but the combination of these into highly integrated visual displays combined with time lapse analysis of operating performance, provide a unique solution not seen in commercial software we have screened.
The paper also provides various real field examples of ESP performance deterioration, showing the impact of polymer deposition leading to increased pump hydraulic friction losses, pump mechanical failure and high motor winding temperature. Diagnoses based on the presented workflow have in many cases been verified by inspection reports on failed ESPs. Diagnosis on ESPs that have not failed cannot be definitive, though the results of remediation (eg pump flush) can help to firm up the probable cause.
Junwen, Wu (Sinopec Research Institute of Petroleum Exploration and Development) | Wenfeng, Jia (Sinopec Research Institute of Petroleum Engineering) | Rusheng, Zhang (Sinopec Research Institute of Petroleum Exploration and Development) | Xueqi, Cen (Sinopec Research Institute of Petroleum Exploration and Development) | Haibo, Wang (Sinopec Research Institute of Petroleum Exploration and Development) | Jun, Niu (Sinopec Research Institute of Petroleum Exploration and Development)
The high efficient foam unloading agent was developed to solve the problem of unloading of liquid loading gas well with high gas temperature, salinity and high concentration of H2S gas and gas condensate. The Gemini anionic surfactant with special comb structure was synthesized as foaming agent molecule, the modified nanoparticles with certain size and degree of hydrophobicity was adopted as solid foam stabilizer, and the fluorocarbon surfactant was designed and synthesised as gas condensate resistance components. The indoor experiment results show that the foam unloading agent showed good foaming and foam stabilizing ability when the temperature is as high as 150°C, salinity is up to 250000 ppm and H2S concentration up to 2000 ppm. Besides, the foam unloading agent present good liquid carrying ability when the volume fraction of gas condensate is as high as 50%. The field test of this foam unloading agent in Longfengshan north 201-XY well shows that, the average gas production increased from 7256 m3/day to 11329 m3/day, increased by 56%, the average differential pressure between tubing and casing dropped from 2.66 MPa to 2.38 MPa, fell by 10.5%, both liquid yield and gas production are obvious, which prove that the foam unloading agent can meet the demand of drainage gas recovery for high content gas condensate gas field.
Chemical EOR is an increasingly employed approach used to enhance oil recovery by combining changes in fluids mobility, macroscopic sweep, interfacial tension, etc. to essentially improve, or extend the economic life of a water flood. It includes flooding with polymer, surfactant, alkaline/surfactant, alkaline-surfactant-polymer (ASP), CO2 and / or other miscible gases which is often combined with waterflood (
The paper evaluates the main chemical changes that occur in the system for each EOR approach –– and shows how these changes, including in situ reservoir reactions and the stability/instability of the EOR packages themselves can exacerbate a range of PC-related challenges especially when considering the likely production of up to three different fluids: formation water, the EOR flood medium and any previous flood water from previous secondary recovery
The paper includes modelling results, laboratory results to validate model predictions as well as examples from field case studies to illustrate the impact of the chemical changes referred to above. Specific highlights include the impact of the use of either high- or low-pH EOR fluids on scale control, corrosion control and asphaltenes control; for scale it examines both inhibitor performance
The overall conclusion is that chemical EOR can have significant impact on PC and that these should not just be considered at the design stage and not just for the injection system but also to take into account the impact these may have on production wells following breakthrough of flood waters, showing that essentially each new or exacerbated PC issues can be predicted or at least anticipated with the required degree of confidence before implementation of EOR.
Chemical enhanced oil recovery (EOR) methods have received increased attention in recent years since they have the ability to recover the capillary trapped oil. Successful chemical flooding application requires accurate numerical models and reliable forecast across multiple scales: core scale, pilot scale, and field scale. History matching and optimization are two key steps to achieve this goal.
For history matching chemical floods, we propose a general workflow for multi-stage model calibration using an Evolutionary Algorithm. A comprehensive chemical flooding simulator is used to model important physical mechanisms including phase behavior, cation exchange, chemical and polymer adsorption and capillary desaturation. First, we identify dominant reservoir and process parameters based on a sensitivity analysis. The history matching is then carried out in a stage-wise manner whereby the most dominant parameters are calibrated first and additional parameters are incorporated sequentially until a satisfactory data misfit is achieved. Next, a diverse subset of history matched models is selected for optimization using a Pareto-based multi-objective optimization approach. Based on the concept of dominance, Pareto optimal solutions are generated representing the trade-off between increasing oil recovery while improving the efficiency of chemical usage. These solutions are searched using a Non-dominated Sorting Genetic Algorithm (NSGA-II). Finally we implement a History Matching Quality Index (HMQI) with Moving Linear Regression Analysis to evaluate simulation results from history matching process. The HMQI provides normalized values for all objective functions having different magnitude and leads to a more consistent and robust approach to evaluate the updated models through model calibration.
Li, Heng (Computer Modelling Group Ltd.) | Dang, Cuong (Computer Modelling Group Ltd.) | Mirzabozorg, Arash (Computer Modelling Group Ltd.) | Yang, Chaodong (Computer Modelling Group Ltd.) | Nghiem, Long (Computer Modelling Group Ltd.)
This paper presents a novel workflow to optimize Alkaline, Surfactant, and Polymer (ASP) flooding under oil price uncertainty. ASP flooding is a relatively expensive recovery process and the optimization approach must be able to account for the change in oil price to provide realistic economical predictions. A workflow, combining time-event optimization with Robust Optimization has been developed and applied to the optimization of the NPV of the ASP process under uncertain time varying oil prices. Several forecasts of the future oil price from different energy organizations and financial institutions were used to account for the uncertainty of the oil price in the NPV calculations. The proposed workflow introduces the parameterization of time-dependent events to identify the best time to start the ASP flooding project after the secondary water flooding, and to determine the size and composition of different chemical slugs. The Robust optimization workflow is compared to the Nominal Optimization workflow, which is carried out using the average oil price forecase to calculate NPV. Results show that Robust Optimization increases the project Net Present Value (NPV) by over 18% compared with Nominal Optimization. Robust Optimization generates optimal ASP design with desirable performance under different oil price forecast scenarios. In addition to ASP flooding, the Robust Optimization workflow can be efficiently applied to other recovery processes for both conventional and unconventional reservoirs.