Decision making in the oilfield is a crucial process in petroleum oilfield activities where numerous attributes and uncertainties exist in the complete process. In the development process of new fields, a well-organized historical database to minimize uncertainty is important as it decreases risks, provides better insight and robustness in decision making. Statistics is a strong tool to turn information or data into knowledge when used with care and physical understanding of the cause-effect relation between the attributes and the outcome. Unfortunately, historical data and learnings from the past cannot be used in an efficient way in oilfield decisions due to lack of systematically organized historical data where there is a huge potential of turning terrabytes of data into knowledge and understanding, for more successful decisions and results. On the other hand, using historical data on reservoir enginnering studies are generally very complex since it requires integration of vast amount data from several diciplines that have different data sources and from different scales, which is sometimes leading modelers to prefer new data collections from the field rather than using complex historical data on hand. In this sense, a multi-attribute based statistical model for each reservoir will greatly enhance the outcomes of future actions through bringing a statistical understanding to a physically more complex relation between the causes and effects or in other words, attributes and results. The data driven models are established for the desired phenomena by means of collecting relevant historical data, after which a multivariate regression is carried out to show the significance of each attribute in the model. These statistical models are then used to make future decisions in the same reservoir in such a way that attributes can be selected within the optimum ranges that yield best results and outcome. Attributes can consist of events and key parameters that influence the outcome. The model is illustrated to investigate the factor affecting the performance of vertical and horizontal wells in tight reservoirs. The data-driven model is validated with the numerical reservoir simulation model and used to determine the significance of each parameter and the optimum operating intervals.
Objectives/Scope Coalbed methane (CBM) has become an important source of clean energy in the recent decades worldwide including the US, China, Australia, India and Russia with more than 60 countries having different degrees of promising coal reserves. CBM reservoirs are distinguished from conventional reservoirs due to the major difference in the mechanism of gas storage and production of water. In CBM reservoirs, pores act as the major storage mechanism as gas is trapped and stored there and produced by means of dewatering and thus lowering the reservoir pressure. Free gas forms as the pressure is lowered leading to increased gas permeability of coal and thus increasing recovery. Microbial activity and thermal maturation of organic compounds are the main mechanisms of methane generation in lower-and-higher rank coals, respectively. Even though methane is an abundant and clean energy source, there are certain operational, technical and economic challenges involved in its production due its unique nature outlined above. Thus, a strong understanding of the parameters and uncertainties that influence the recovery is crucial. Methods, Procedures, Process Due to the fact that the organic materials that make up coals generally have a stronger affinity for CO2 than for methane, CO2 is used as an enhanced recovery method to displace methane as an enhanced coalbed methane recovery (ECBM) method. While there is no current comprehensive optimization study on the effects of such factors, ECBM has a very significant role in the future of energy as it means more energy out of natural gas while eliminating the adverse effects of greenhouse gases. Results, Observations, Conclusions In this study, a standard SPE reservoir simulation model is used to study the factors influencing the recovery in coal bed methane reservoirs by investigating the significance of parameters including but not limited to porosity, adsorption capacity, fracture permeability along with coal density and irreducible water saturation. Novel/Additive Information The optimization results obtained by means of coupling a full-physics commercial numerical reservoir simulator with an optimization/uncertainty tool are presented outlining the different degrees of significance of these factors on production and ultimate recovery for better understanding of the phenomenon that will lead to more robust reservoir management decisions.
With the increasing use of reservoir simulation, not only in conventional reservoir modeling, but also in complex and large reservoirs, the need for faster and more robust modeling of reservoirs, along with the facilities and surface networks, has become crucial to meet the requirements of modeling either large or multiple sub-reservoirs with thousands of wells and surface networks, including pipelines and gathering centers, connected all the way to the separators. Next-generation reservoir simulators have begun replacing conventional reservoir simulators over the last decade in conjunction with the addition of more features that enable them to model different phenomena of reservoir fluid flow and recovery techniques, putting them at the forefront of reservoir simulation. While next-generation simulators offer more convenient solutions through surface-subsurface coupling and faster simulations that save time in decision-making processes in reservoir management, there are still some difficulties for the user, as well as in the development of these simulators from a computational point of view. The objective of this study is to outline the strengths and weaknesses along with the challenges in this process to serve as a guide for more efficient use. This study presents an analysis of an important component in the reservoir-network-coupling process in which the reservoir unknowns and network equations are solved iteratively. In the Schwarz procedure, the overlap region consists of reservoir cells included in the network set. An optimum solution for the number of cells extended into the network is important, because as the number of reservoir cells included in the network increases, the cost of the network to solve increases, which can become quite expensive, since bigger and more complex networks are needed to find the solution. This study highlights the effect of using different options for a better understanding of the important components of the coupling process; the results serve as a guide for a more efficient use of this procedure. Reservoir simulation involves several complex calculations to model the full-physics of the subsurface flow, as well as the well and surface facilities. It is not straightforward and easy to model and computationally evaluate the modeled phenomena. Discretizations and approximations contribute to error. Run time is another component that must be managed to find a balance between accuracy, precision, and time. Literature lacks practical examples of the Schwarz procedure, which is another important component in coupling reservoir and surface networks that should be used wisely and efficiently to find a balance between accuracy and computational time. A solid, practical example with the theoretical background that this study outlines will benefit the users of complex and large reservoir simulation models.
Diatomites are high-porosity, low-permeability reservoirs with elastoplastic properties and high geo-mechanical responsiveness. Despite that, diatomites have great potential for oil recovery. Withdrawal of fluids from the reservoir rock leads to subsidence causing compaction and shear stresses. This disturbed stress distribution results in well failures that causes loss of millions of dollars. Successful maintenance of pressure support through optimum injection/production is key to preventing subsidence to mitigate the risk of well failure and achieve better sweep efficiency for recovery. There have been different approaches to tackle subsidence and well failures in diatomites including the use of ‘backpressure method’ coupled with a neural network to optimize injection-production to ‘balance’ the rock in terms of stress-distribution and thus decrease well failure due to shearing. However, using such methods may mask other problems the well is experiencing, such as, mechanical issues that influence production. Another existing approach, satellite-imaging (InSAR) cannot be used to take real-time actions that is crucial in diatomites. Surface tiltmeter data is collected to undertsand the relationship between injection/production and resulting surface deformation, which also provides information about well-to-well connectivity. A neural network-based approach is followed to determine the nonlinear relationship between surface subsidence/dilation and injection-production. This is then used to build an objective function that works to minimize the differences between well-to-well subsidence/dilation measured by the tiltmeters, by adjusting injection-production for the wells. In this paper, a method that harnesses real-time surface tiltmeter data to adjust injection-production distribution in diatomites to decrease well failures is used beyond the existing applications of surface tiltmeter, such as, in the areas of detection of early steam breach to surface in steam operations and fracture orientation and it provides real-time data for robust reservoir management of such reservoirs where satellite imaging is not effective.
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