This paper addresses sampling and PVT modeling of liquid-rich fluids produced from ultra-tight formations – "liquid-rich shale" (LRS) reservoirs.1 Proper PVT treatment in these unconventional reservoirs is important to provide improved short- and long-term oil and gas production forecasts, and define the initial oil and gas in place. We give recommended practices for sampling, laboratory PVT tests, developing PVT (EOS and black-oil) models, and estimating the in-situ reservoir fluid system (composition, saturation, and initial gas-oil ratio). Fluid systems studied include a wide range from lean gas condensates to volatile oils, typical of what is found in the Eagle Ford, Avalon, and other liquid-rich shale plays in North America – with producing oil-gas ratios ranging from 10-1,000 STB/MMscf. LRS producing wellstreams, usually expressed in this paper as a producing oil-gas ratio (OGR) or "liquid yield" 2, are always much leaner than what would be produced from a conventional, higher-permeability reservoir containing the same initial reservoir fluid system. Conventional reservoirs typically produce an initial mixture (for months or years) that is quite similar to the in-situ initial reservoir fluid. The anomalously-low producing OGR of LRS wells is due to very low permeabilities that lead to large drawdowns and fluid flow with localized and large gas-to-oil mobility ratio gradients near the fractures. We show that the loss in oil is a staggering factor of 2 to 50! The liquid yield will be approximately constant from the early days of initial testing throughout the well's entire life. The degree of oil recovery in LRS wells is associated mainly with two issues. First and foremost, whether the reservoir is initially saturated with oil (Soi=1–Swc) or gas (Soi=0). For example, with the in-situ solution OGR of ~350 STB/MMscf (initial GOR of ~3,000 scf/STB), the producing OGR might be 100 STB/MMscf for an oil reservoir (Soi=1–Swc), while it might be less than 10 STB/MMscf for a gas reservoir (Soi=0). Second, for oil LRS reservoirs, oil recovery loss is greatest for near-saturated initial conditions, with oil recoveries increasing as the oil reservoir becomes more undersaturated; degree of undersaturation does not have an impact on the large oil recovery losses seen in all LRS gas reservoirs. Another important result from our study is showing how liquid yield (OGR) evolves with time for LRS wells. It is shown for planar "slab" fracture geometries that the expected infinite-acting behavior is a constant OGR that may last many years or decades. A less-constant intermediate-to-long-term OGR development is found in naturally- or induced-fracture "networks" consisting of a collection of matrix blocks surrounded by fractures. OGR variation depends on network fracture "block" size. The paper shows that it is necessary to combine single-well, finely-gridded numerical modeling of LRS wells to properly develop valid PVT models and in-situ fluid description. Conventional "PVT" sampling and initialization procedures are alone inadequate for liquid-rich shale systems, but additionally require proper treatment of near-well reservoir flow and phase behavior to properly link the significant contrast in producing wellstreams and in-situ fluids. Finally, we propose a special PVT laboratory test for LRS systems.

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The main objective of this paper is to present a model based integration platform to perform 60+ oil wells optimization in PETROBRAS Urucu field (Solimões Basin, Amazon Rain Forest), while honoring operational constraints on wells, production separators, pipelines, and total field gas processing capacity. The solution was built based on current technologies of modeling gas and water coning in the high-permeability reservoirs of Urucu, including detailed 3D finite-difference well models automatically history matched to production data, and empirical models based on fundamental flow theory. The well models have been integrated into a prototype solution to simultaneously solve for all 60+ optimal well rate controls. The optimization scheme should determine the optimum setting of well control (e.g. liquid rate) of all the wells in the field and advise on production management of each individual well. The developed concept is presented in the paper. Another important development presented is a virtual metering modeling that estimates production rate of each well, based on real time data of pressure and temperature measurements on wellhead. The Urucu field optimization strategy relies intensively on the engineering work related to process and technology. An international and multidisciplinary group including academic collaboration was established to compose the various knowledge needed to embrace the challenge.

Developing Oil & Gas assets requires planning production on multiple horizons: (1) the long-term production plan includes strategic decisions for technology and recovery strategies to maximize the Net Present Value (NPV) of the project, (2) the mid-term horizon includes the drilling program and reservoir depletion/injection rates and (3) the short-term optimization (real-time production optimization or RTPO) aims to maximize the usage of the existing facilities. In the case of RTPO, both subsurface and surface systems are important, the goal being to maximize the daily production while honoring all operational constraints. RTPO requires a comprehensive integrated model covering the entire production system and an accurate mathematical formulation of the problem. This implies finding an appropriate optimization strategy and solver to find an optimal solution within a reasonable time. Sustainable production optimization solutions also assume continuous model update, maintenance and improvement, as the production system behavior changes over time. In this paper, we develop an integrated model for a complex multi-field asset. The production system includes 12 gas wells, 24 gas-lifted oil wells, 4 gas-injection wells, 4 CO2-injection wells, subsea manifolds, gas pipelines, offshore process facilities and CO2 removal units. Gas production from each field is gathered in a single gas pipeline system connected to a gas processing facility located onshore. Control variables include wellhead pressures, routing of wells, gas lift rates, flaring and re-injection rates. Many capacity, pressure and compositional constraints are considered through the whole production system. The production optimization model including binary variables and non-smooth non-linear functions is rather challenging to solve. Each part of the integrated model is approximated with multidimensional piecewise-linear functions to a desired degree of accuracy. The resulting Mixed Integer Linear Program (MILP) can be solved efficiently with existing commercial solvers. The optimization solution is used to answer different types of challenge: (1) platform start-up, (2) unexpected failure of a gas compressor, (3) maintenance on a group of wells and (4) changing reservoir conditions. Production increase driven by RTPO ranges from 1 to 5 % with no additional CAPEX. The implementation of the production optimization solution is also discussed. The importance of the usability, user training and solution maintenance is highlighted.