Viscous oils have adverse mobility that causes production to decline rapidly following a period of primary recovery. Traditionally, enhanced oil recovery (EOR) for this kind of oil has mostly relied on reducing viscosity and increasing mobility using thermal methods or miscible gas injection. However, in some cases—for example, deep reservoirs, thin sands, or restricted offshore applications—these methods might not be feasible. This paper discusses published field studies using polymer flooding EOR for viscous oil and provides recommendations for its applicability.
In countries with reservoirs that present challenges for use of traditional EOR methods, such as Canada, China, and Suriname, polymer flooding has been field tested as a reliable oil-sweeping method while minimizing the risk of high water cut. With oil viscosities at reservoir conditions up to 5000 cp and reasonable economic conditions, polymer flooding has been shown to be an attractive EOR alternative. The study cases presented show that polymer concentrations need only be between 800 to 1,500 ppm to ensure successful results.
Concerns about low or poor injectivity of polymers are being overcome using fracturing and horizontal wells, while high-value, limited-space challenges on offshore platforms are being addressed through modular and minimized installations. Additionally, most conventional waterflood monitoring and surveillance techniques are also applicable to polymer floods, while polymer use in backflow tests and as a tracer has also been proposed.
This study's projection shows a 90% probability of positive net present value (NPV) under broad ranges of uncertainty for oil price, recovery factor (RF), capital expenditures (CAPEX), and operational expenditures (OPEX). The results of this paper show that polymer flooding presents a clear and feasible alternative for increasing the RF of viscous oil. To this end, a detailed study is provided of its advantages and the reservoir condition range of applicability.
Selecting the optimum combination of enhanced oil recovery (EOR) technologies is a critical activity during preparation of a successful business plan. This is usually accomplished by using protocols with technical parameters at reservoir formation levels from which candidates EOR technologies are screened and then
subject to investment and risk assessment evaluation for final decision by management.
The previous approach very often misses the effect of operational expenditures, wearing and failure related downtime and learning curve during pilot testing on oil recovery efficiency and EOR economics which is usually expressed in terms of net present value.
This paper proposes an alternate simplified approach using an acceptable representation of life cycle phases of natural and physical asset components functioning as a system of assets for a particular subject reservoir. We introduce a taxonomic solution to help in classifying candidates EOR technologies. Four major functional indices are used for handling uncertainties and risks using data from analogs for defined scenarios in the subject reservoir: 1-Accesability) Footprint effects for constructing and operating physical assets (wells and surface infrastructure), 2- Contactability) Volume of resources contacted from a surface
location, 3-Produceability) Volume of producible resources from drainage area to surface and 4- Effectiveness) Combined effect of availability, reliability, maintainability y and capability in efficiency of oil recovery.
The four major indices are cross referenced with life cycle cost (LCC) and estimated ultimate recovery (EUR) using Hubbert peak oil theory and the Petroleum Resources Management System classification for resources and reserves. Uncertainties and risks are modeled with Monte Carlo simulation (MCS) or
Systems Dynamics (SD) depending on the complexity of the system of assets.
We present examples using synthetic data to illustrate the method.
This approach is worth using during EOR project definition and planning as an alternate to complex data hungry methods.
Most of the world's oil is found in a small number of ‘petroleum systems'. A petroleum system is an area where oil or gas was created in a single ‘pod' of source rock which has migrated upwards and become trapped in a number of oil and gas fields or assets. Not only is oil concentrated mainly in very few provinces
and petroleum systems within these provinces, but within a given petroleum system, it is concentrated inside very few fields (1).
Therefore it is a fact that the oil and gas industry deals with the management of a finite, depletable or exhaustible resource, i.e., hydrocarbon resources.
The production of any hydrocarbon resources from a field or asset has a gradual increase (development phase) reaching a maximum output (peak), then a plateau and a decline (maturity) when the output will fall irreversibly down to an economic limit where the decision of abandonment needs to be addressed. Many fields combine together, placing a small number of large fields near the beginning and a large number of small fields at the end produce something like a bell curve as shown in Figure 1. This bell curve is also called Hubbert Peak Curve after the geophysicist M.King Hubbert who predicted in 1956 (2) (3) that the oil production for the lower 48 states of USA would peak between 1965 and 1970.
Hubbert recognized that the depletion of any finite resource starts with production at zero and that in the initial stages, the rate of oil production tends to increase exponentially with time. However, physical limits prevent production to increase at the same rate because finite resources cannot keep up the increasing production rate forever. At the end of the life cycle the total volume under the bell curve will be the ultimate recovery.
The magic of Hubbert´s approach was the use of a proxy model known as the logistic equation, developed by the Belgian statistician Pierre Verhulst which was an improvement over the Malthusian model (4). In 1982, Hubbert refers to the original Verhulst papers and goes through a complete derivation of the
mathematics involved in his model. The details and implications of the use of proxies for modeling management of finite resources is beyond the scope of this paper, however we address the issue from an economic perspective later in the paper. For now we can state that Hubbert Peak curve can be used to
empirically approximate the full cycle of the growth, peaking, and subsequent decline to zero of the production of a finite, nonrenewable resource and estimate ultimate recovery.