Discovered resources of heavy and extraheavy crude oil are estimated to be approximately 4,600 billion bbl, two-thirds of which are in Canada and Venezuela. Bitumen and tar sands are excluded from this estimate. Published data on reserves estimates (RE) from this resource by primary drive mechanisms are sparse. Meyer and Mitchell estimated worldwide ultimate recovery from heavy and extraheavy crude oils to be 476 billion bbl, which is 10% of the Briggs et al. estimate of the discovered resource initially in place. Estimated primary reserves estimates (RE) ranges from 8 to 12% oil-in-place (OIP) for the Orinoco area of Venezuela, where stock-tank gravities range from 8 to 13 American Petroleum Institute (API).
Heavy oil is defined as liquid petroleum of less than 20 API gravity or more than 200 cp viscosity at reservoir conditions. No explicit differentiation is made between heavy oil and oil sands (tar sands), although the criteria of less than 12 API gravity and greater than 10,000 cp are sometimes used to define oil sands. The oil in oil sands is an immobile fluid under existing reservoir conditions, and heavy oils are somewhat mobile fluids under naturally existing pressure gradients. Unconsolidated sandstones (UCSS) are sandstones (or sands) that possess no true tensile strength arising from grain-to-grain mineral cementation. Many heavy oil reservoirs are located in unconsolidated sandstones.
Cold heavy oil production with sand (CHOPS) is a relatively recent technology. As such, only a few case histories of its application over a number of years have been published. Nonetheless, those that are available provide insight into the application of this technology. A detailed Luseland field case history has been published. It had a long history (12 to 15 years) of slow production with reciprocating pumps, an attempt to produce with horizontal wells (6 wells, all failures), and then a conversion to CHOPS through reperforation and progressing cavity (PC) pump installation.
Drilling automation differs from rig automation. Today, this involves the linking of surface and downhole measurements with near real-time predictive models to improve the safety and efficiency of the drilling process. SPE volunteers formed the Drilling Systems Automation Technical Section (DSATS) in 2008. The purpose of DSATS is to accelerate the development and implementation of drilling systems automation in well construction by supporting initiatives which communicate the technology, recommend best practices, standardize nomenclature and help define the value of drilling systems automation. Appropriate initiatives include workshops, forums, lectures, and technical and white papers, among others, and DSATS actively encourages participation of automation experts from outside the drilling industry.
Cold heavy oil production with sand (CHOPS) recovery processes generate large volumes of sand that must be managed. In Canada in 1997, approximately 330,000 m3 of sand (approximately 45% porosity sand at surface) were produced from CHOPS wells. Individual wells may produce as much as 10 to 20 m3/d of sand in the first days of production and may diminish to values of 0.25 to 5 m3/d when steady state is achieved. Sand grain size reflects most of the reservoir. There is little sorting or segregation in the slurry transport to the well; however, not all zones in the reservoir may be contributing equally at all times.
On behalf of the Society of Petroleum Engineers (SPE), we take great pleasure in inviting you to partner with us as a sponsor and/or exhibitor. The SPE Canada Unconventional Resources Conference and the SPE Canada Heavy Oil Technical Conference will take place 18-19 March 2020 at the BMO Centre at Stampede Park in Calgary, Alberta. By co-locating these two conferences, attendees will have access to both technical programs and a shared exhibit floor. Don’t miss the opportunity to enhance your company presence and support the oil and gas industry. Sponsorship and exhibition opportunities are both available.
Since decades, steam-assisted oil recovery processes have been successfully deployed in heavy oil reservoirs to extract bitumen/heavy oil. Current resource allocation practices mostly involve reservoir model-based open loop optimization at the planning stage and its periodic recurrence. However, such decades-old strategies need a complete overhaul as they ignore dynamic changes in reservoir conditions and surface facilities, ultimately rendering heavy oil production economically unsustainable in the low-oil-price environment. Since steam supply costs account for more than 50% of total operating costs, a data-driven strategy that transforms the data available from various sensors into meaningful steam allocation decisions requires further attention.
In this research, we propose a purely data-driven algorithm that maximizes the economic objective function by allocating an optimal amount of steam to different well pads. The method primarily constitutes two components: forecasting and nonlinear optimization. A dynamic model is used to relate different variables in historical field data that were measured at regular time intervals and can be used to compute economic performance indicators (EPI). The variables in the model are cumulative in nature since they can represent the temporal changes in reservoir conditions. Accurate prediction of EPI is ensured by retraining regression model using the latest available data. Then, predicted EPI is optimized using a nonlinear optimization algorithm subject to amplitude and rate saturation constraints on decision variables i.e., amount of steam allocated to each well pad.
Proposed steam allocation strategy is tested on 2 well pads (each containing 10 wells) of an oil sands reservoir located near Fort McMurray in Alberta, Canada. After exploratory analysis of production history, an output error (OE) model is built between logarithmically transformed cumulative steam injection and cumulative oil production for each well pad. Commonly used net-present-value (NPV) is considered as EPI to be maximized. Optimization of the objective function is subject to distinct operating conditions and realistic constraints. By comparing results with field production history, it can be observed that optimum steam injection profiles for both well pads are significantly different than that of a field. In fact, the proposed algorithm provides smooth and consistent steam injection rates, unlike field injection history. Also, the lower steam-oil ratio is achieved for both well pads, ultimately translating into ~19 % higher NPV when compared with field data.
Inspired from state-of-the-art control techniques, proposed steam allocation algorithm provides a generic data-driven framework that can consider any number of well pads, EPIs, and amount of past data. It is computationally inexpensive as no numerical simulations are required. Overall, it can potentially reduce the energy required to extract heavy oil and increase the revenue while inflicting no additional capital cost and reducing greenhouse gas emissions.