The goal of this project is to develop a variety of technologies for petroleum production and exploration enhancement in deep water and mature fields. The three primary objectives are (1) to advance the design of new drilling fluids that can address the problem of excessive equivalent circulating density at deep water drilling locations; (2) to develop a neural network model for CO2 enhanced oil recovery (EOR); and (3) to develop tools for rapid interpretation of marine controlled-source electromagnetic (CSEM) data. CSEM technology shows great promise as a method for directly indicating the presence of hydrocarbons in areas where seismic surveying is problematic.
University of Louisiana at Lafayette (UofL@L), Lafayette, LA.
A unique problem of drilling oil and gas wells in deep water is the narrow window between the formation pore pressure and fracture pressure. Figure 1 shows a typical case from offshore Ghana (Hradecky and Postler, 2004). The traditional drilling practice with a constant-gradient drilling fluid requires excessive casing programs and larger, more expensive rigs to drill the formations. For the case shown in Figure 1, at least six casing programs are required to open and drill through the pay zone. Still, some 30 to 50% of the world’s known offshore oil and gas resources cannot be developed due to excessive equivalent circulating density, which causes the formation to break down (fracture) during drilling. This problem has been generally recognized by the petroleum industry and well documented (Hannegan and Wanzer, 2003).
The present use of fossil fuels has raised concern about the increasing CO2 concentration in the atmosphere. The consensus is that CO2 emission levels may be one of the major causes of climate change and efforts are being made to regulate the emissions of greenhouse gases. During the 1992 UN Conference on Environment and Development in Brazil, a long-term goal to avoid an undesired and uncontrolled change in climate was established (Lindeberg, Erik, Holt, and Torleif, 1994). Extensive CO2 storage in underground reservoirs has been proposed as an option to reduce the net emissions. As a value-added benefit to CO2 sequestration and storage, CO2 injection into depleted oil reservoirs can result in the recovery of oil and gas resources left behind by earlier recovery efforts. CO2 at supercritical pressure has the viscosity of a gas but the density of a liquid, penetrating tight rock formations and pressurizing depleted reservoirs, leading to recovery of incremental oil. Various miscible agents, including CO2, have been used to produce incremental oil varying from 7–23%, with an average of 13.2% (Martin and Taber, 1992; Moritis, 2003). Oil recovered with CO2 injection yielded approximately 63% of oil initially in place compared to approximately 43% for water injection in Norway (Lindeberg, Erik, Holt, and Torleif, 1994). This project will study flue gas/CO2 huff and puff in candidate Louisiana oil reservoirs. Some of these reservoirs’ low-viscosity oils (with oil API gravity ranging between 25 to 48 oAPI) are best suited for miscible gas flooding. Flue gas (15–30% CO2) and the huff and puff production mechanism were chosen due to the possible lack of a CO2source.
Marine controlled-source electromagnetic geophysical surveying (MCSEM) has garnered much recent attention as a relatively unexplored but promising hydrocarbon exploration tool. Commercial interest hinges largely on the evidence of MCSEM as a lower risk, less expensive hydrocarbon indicator in comparison to seismic indicators (DHIs) and well exploration in deep water (Constable and Srnka, 2007). Despite the optimistic appraisal, the MCSEM approach is not without problems, many of which are associated with modeling the difficult physics behind electromagnetic induction.
Typically, the model physics is approximated by a simple one- or two-dimensional model or a computationally expensive three-dimensional exact approach generally based on a finite element or finite difference algorithm. An approach wherein complexly shaped, electrically heterogeneous Gulf of Mexico reservoirs are modeled and simplified to approximate homogeneous bodies has been developed. Previous work (Aliamiri, et al., 2007) has demonstrated that inversion and classification vital to risk assessment can be successfully achieved using a computationally efficient approximate model (even when the assumptions of the approximation were somewhat violated) if the model parameters were described probabilistically. In order to build probabilistic reservoir descriptions, a suite of data from known reservoirs has been gathered. These descriptions will allow the project to simplify modeling, invert efficiently, characterize expected Gulf of Mexico CSEM reservoir responses, and quantitatively evaluate current interpretation techniques. In essence, the probabilistic approach provides a flexible, workbench-like test bed for Gulf of Mexico CSEM research.
This project includes three very different focus areas. Successful R&D in each of these areas will result in a variety of beneficial impacts.
Development of Heavy Foam as a Drilling Fluid Alternative for Deepwater Wells
This research will help overcome the lack of a means for testing the properties of heavy foam. The testing apparatus developed during this project and the data produced will result in formulations of heavy foams that will have properties suitable for drilling deepwater wells. Use of heavy foam could significantly reduce the cost of drilling in deep water and thus the development costs for deepwater oil and gas resources. This could lead to as much as a 30 to 50 % increase in recoverable oil and gas reserves from U.S. deepwater offshore fields.
Development of a Neural Network for CO2 EOR
This portion of the research will identify candidate oil reservoirs in Louisiana for CO2/flue gas enhanced oil recovery using an artificial neural network model. The developed expert system will be used in mapping the non-linear relationships between different input parameters, namely reservoir and fluid properties and project design parameters. The generated results (such as breakthrough time, amounts of injected gases, and oil recovered, etc.) can then be applied to different Louisiana fields to generate similar mappings without going through cumbersome and lengthy numerical simulation studies. The oil industry will utilize the proposal results to embark on pilot studies, partial field projects, and perhaps full EOR project developments. CO2/flue gas injection in depleted, low-pressure, lower-temperature reservoirs will contribute to incremental oil production, lead to increased domestic oil reserves, and help sustain oil production in Louisiana. The increased production will help reduce U.S. dependence on other oil markets, help to maintain exploration and production efforts, and sustain and strengthen the oil industry job market in the state of Louisiana for years to come. CO2-EOR in high-temperature, high-pressure reservoirs is also suggested. In addition to sequestrating CO2 to mitigate greenhouse effects, incremental oil production from 10% to 20% of original oil in place is plausible.
Development of CSEM as an Exploration Tool
This research will provide insight into the expected marine controlled-source electromagnetic (MCSEM) response of a hydrocarbon-bearing Gulf of Mexico reservoir. The technology could be extended to other regions of interest given an appropriate library of reference reservoir geometries. MCSEM has been embraced by many in the industry sector due to its ability to directly indicate the presence and quality of hydrocarbons, and for its potential to image an area in which seismic surveying is problematic. The research will develop a simplified model, a Bayesian inverse model, and an easily extensible, simplified library of Gulf of Mexico reservoir descriptions derived from known reservoir data. Monte Carlo simulation will produce synthetic responses from a broad range of possible Gulf reservoirs, from which a quantitative, model-based description of the variation of the Gulf MCSEM response will be derived. This description will provide a much needed intuitive understanding of the expected performance of MCSEM in the Gulf, will constrain maximum likelihood and Bayesian inversion, and provide a basis for estimator bounds computation and sensitivity analysis.
The project has been completed. The final report is available below under "Additional Information".
Final Project Report [PDF-6.72MB]