Silvya Dewi Rahmawati

Cyclic Shut-in Eliminates Liquid-Loading in Gas Wells

This paper presents a method to eliminate production loss due to liquid-loading in tight gas wells. Cyclic shut-in control is a simple production strategy that particularly benefits lower-permeability stimulated wells, including but not limited to shale gas wells. Comparison is made between a gas well producing (1) in a “ideal” situation where 100% of liquids entering from the reservoir or condensing in the tubing are continuously removed (without shut-ins), (2) in a meta-stable liquid-loading condition with low gas rate, typical of most wells today, and (3) by the proposed strategy of cyclic shut-in control. We show that cyclic shutin control of stimulated low-permeability vertical wells to ultra-low-permeability horizontal multi-fraced wells can produce without ever experiencing liquid loading, and with little-to-no delay of ultimate recovery. Cyclic shut-in control can be applied to all stimulated, lower-permeability gas wells, from the onset of gas rates that result in liquid-loading. The strategy can also be used for wells which already have experienced a period of liquid-loading , but the expected performance improvement may be less because of near-well formation damage caused by historic liquid-loading – e.g. fresh-water backflow and liquid-bank accumulation. In historically liquid-loading wells, an initial period of liquid removal and/or light stimulation may be needed prior to initiating cyclic shut-in control. We show that the shut-in period should optimally be as short as operationally possible. Cyclic shut-in control is shown to work equally well for layered no-crossflow systems with significant differential depletion at the onset of liquid loading. Minimizing rate and recovery loss of liquid-loading gas wells is of international interest. We believe that cyclic shut-in control will become an industry standard practice for shale gas wells, and should lead to a significant ultimate increase in worldwide gas reserves. The method is extremely simple and requires only a rate-controlled wellhead shut-in device.

Multi-Field Asset Integrated Optimization Benchmark

Integrated modeling of multi-field assets, from subsurface to market, is challenging due to the complexity of the problem. This paper is an extension of the SPE 121252, model based integration and optimization gas cycling benchmark [Juell, et al., 2009], extending two gas-condensate fields to two full-field multi-well models. Additionally, a full-field model is added to the Juell benchmark, introducing an oil field undergoing miscible WAG injection, where most data are taken from the SPE 5 Reservoir Simulation Comparative Project. All reservoir models are compositional, but using different EOS representations. A base case scenario is defined with fixed numbers and locations of producers and injectors. A common field-wide surface processing facility is modeled with emphasis on water handling, NGL extraction, sales-gas spec, and gas reinjection. The surface process model interacts with the three reservoir models through two main mechanisms – (1) waterand gas-handling constraints, and (2) distribution of available produced gas for reinjection into the three reservoirs. The field asset model provides long-term production forecasts of gas, oil, and NGL revenue. Cost functions are introduced for all major control variables (number of wells, surface facility selection and operating conditions, injection gas composition). Net present value is used as the target objective function. This paper will evaluate optimal production strategies for the base case benchmark problem, using several key control variables and field operational constraints. Optimization performance will be tested with a few solver algorithms. The benchmark will be provided to the industry through application data files, network infrastructure, and results from our integrated optimization model. Introduction Operation of complex assets may require a holistic view of the value chain. This is particularly important if the different parts of the value chain are tightly connected. Present industrial practice typically takes a silo approach in the sense that one part of the supply chain is treated quite separate from other parts. This is pronounced in the upstream area where for instance a decision support application for optimally allocating well production may include well and pipeline models. The downstream boundary condition is typically a constant pressure at the inlet separator. Similarly an optimizer for the surface process does not include models of the upstream system. This implies that the inlet separator acts as a “dividing wall” between two optimizers even though the two subsystems might be tightly connected. An example of this is when the gas output from the surface facility is fed back into the upstream system through gas-lift wells or gas injectors. There are many reasons for the silo-like situation. Different parts of the supply chain recruit people with different backgrounds and they use quite different decision support tools. This limits integration even in situations where integration has an obvious potential. Several researchers have conducted research on various integration topics. [Bailey et al., 2005] and [Cullick et al., 2003] discussed complex petroleum field projects applying uncertainty analysis, but the surface process facility was not considered. [Nazarian, 2002] integrated ECLIPSE and HYSYS simulators to calculate integrated field operation in a deepwater oil field. 2 [SPE 130768] Those simulators were coupled by using Automation and Parallel Virtual Machine and applying a genetic algorithm for the optimization. [Hepguler & Barua, 1997] and [Hepguler et al., 1997] discussed an integrated application for reservoir-production strategies and field development management. In this case, the ECLIPSE reservoir simulator was coupled with the surface and production network simulator and the optimizer (Netopt). Run time can be a challenge in integrated application, especially when closely linked high-fidelity models are tightly connected. [Barroux et al., 2000] proposed a practical solution to reduce run time of the coupled simulators. [Trick, 1998] applied a somewhat different procedure from [Hepguler et al., 1997], using the same interface. In this case an ECLIPSE black oil reservoir simulator was coupled to a surface gas deliverability forecasting model, FORGAS. The use of integrated optimization in a day-to-day operations setting of the LNG value chain was studied by [Foss and Halvorsen, 2009)]. To reduce computation time they chose simple models for all system components. A sizable gain could be identified by integrating all models into one decision support application as opposed to dividing them into two applications; one for the upstream part and the other for the LNG plant. [Tomasgard et al., 2007] presents a natural gas value chain model and integration applying an upstream perspective and a stochastic portfolio optimization. The literature citings above identifies a potential for integrating models in decision support tools. Moreover, integrated simulation and optimization is clearly regarded as an interesting but challenging topic. Hence, in this paper we present a benchmark problem which is designed to assess the potential of an integrated approach in decision support tools. A realistic benchmark as well as a base case will be defined in the following sections. Further, a sensitivity analysis of key decision variables will be presented in addition to some early optimization results. The paper ends with some conclusions and directions for further work. Integrated Model The model presented in this paper is rich and complex enough to represent the value chain from reservoir to export and thus suitable as a benchmark for integrated operations and optimization (I-OPT). The upstream part of the I-OPT model includes two gas-condensate reservoirs and an oil reservoir while the surface process system includes gas and liquid separation as well as an NGL plant. The model also includes an economic component as indicated in Fig. 1. All model components have been designed using realistic assumptions and parameter values. Further, the project is designed with close links between the upstream and downstream parts of the model, partly due to gas re-injection. This is important since the I-OPT model is designed to study and assess the business value of integrated optimization as a decision support method. Integrated optimization in this context is defined as applications which utilize several different models along the value chain, for instance a reservoir model and a surface process model, in one optimization-based application as opposed to two separate applications for the reservoir and surface part, respectively. Hence, the I-OPT model is designed to challenge the conventional silo approach. The I-OPT model is further designed to study decisions both on a life-cycle horizon as well as shorter time frames. The surface facility model is a steady-state model while the reservoirs are modeled using dynamic models to account for depletion effects. The model is an extension of the full-field model from a previous paper [Juell, et al., 2009]. The I-OPT model will be presented in the following sections. Complete documentation of the I-OPT model including the base case discussed later will also be made available. Reservoir Description The reservoir models include two gas-condensate reservoirs and an oil reservoir. The gas-condensate reservoirs are scaled up from [Juell, et al., 2009] and the oil reservoir is a scaled up version of a miscible WAG project [Killough and Kossack, 1987]. In the base case each reservoir is producing through 5 production wells and injection operations are conducted through 8 injection wells which perform gas injection wells in the gas-condensate reservoirs and WAG injection in the oil reservoir. The production and injection wells are perforated through all layers. The well locations for each reservoir are shown in Fig. 2(b) and are given in Table 10. The gas-condensate reservoir models consist of 36 36 4 grid blocks and the oil reservoir 35 35 3 grid blocks. The horizontal permeability distributions for the three reservoirs vary from a low value in the south west region towards higher permeability values in the north east. This is shown for one layer in Fig. 2(a). The permeability distribution range is presented on Table 1. There are two faults in the horizontal direction, one is non-communicating and the other is partially communicating. The non-communicating fault separates low permeability and medium permeability areas. The partially communicating fault separates the medium and high permeability areas. The non-communicating shale in the vertical direction occurs between layers 3 and 4 in [SPE 130768] 3 the lean gas-condensate reservoir, between layers 1 and 2 in the rich gas-condensate reservoir and between layers 2 and 3 in the oil reservoir. The reservoir models are compositional. The composition for the gas-condensate reservoirs consist of 9 components and the composition for the oil reservoir consists of 6 components. The initial fluid composition for the gas–condensate reservoirs are referred to [Juell, et al., 2009] and for the oil reservoir is presented in Table 7 to Table 9. The compositional reservoir models are run using the SENSOR reservoir simulator. Fig. 1 – Integrated optimization schematic. (a) Transmissibility distribution for lean gas-condensate reservoir in first layer. (b) Production and injection well placement for the lean gascondensate reservoir. Fig. 2 – Reservoir description of heterogeneity and well placement. PVT Description Compositional reservoir modeling usually offers better accuracy than black oil reservoir modeling, but in many cases a black oil model is still preferred due to shorter computation time. Therefore, Black Oil Tables (BOT) are supplied as an alternative to the EOS PVT models. BOT is generated by Constant Compositional Expansion (CCE) experiment for the same surface process us

Experimental Evaluation of Carbonated Water Injection to Increase Oil Recovery Using Spontaneous Imbibition

Carbon dioxide flooding is known for increasing the production of oil as enhanced oil recovery (EOR). Conventional carbon dioxide flooding aims to reach minimum miscibility pressure (MMP) before altering oil properties in the reservoir. However, current conditions are that most fields have reached the mature state, so the reservoir pressure is depleted, thus it is hard to reach MMP. Carbonated water is water into which carbon dioxide has been dissolved, under certain conditions. The carbonated water injection (CWI) technique might be a solution for injecting carbon dioxide bellow MMP. The performance of this technique is examined using a physical model designed to show the wettability alteration mechanism of carbonated water. The physical model was made from a glass chamber filled with unaltered water. A saturated core was then placed inside the chamber below a funnel shaped narrow tube that read the oil recovery for each milliliter scale. The chamber was sealed and carbon dioxide injected into the water body. The water inside the chamber was therefore altered to become carbonated water after a period of soaking time. The oil expelled from the core was spontaneously measured by reading the scale on the top of the graduation tube, which showed how the milliliters oil was gathered, a process that known as imbibition. The process repeated for several concentrations of carbon dioxide in water. The change of injection pressure, power of hydrogen (pH), and oil recovery were measured respected to soaking time. The value of every case, including the unaltered-water-saturation case, were compared. This injection technique could result in 0–37% oil recovery.

CO2 and Methane Separation Using Finger-Type Slug Catcher at Seabed

Gas production with a very high CO2 content requires special treatment to separate the methane from CO2 . The separation also requires high capital expenditure (CAPEX) and operational expenditure (OPEX). The challenge is higher, when the gas is being produced on offshore, and only narrow space that available on the platform for separation process. One proposed method to do the separation is by shifting the CO 2 phase in the phase diagram from the gas phase to the li q uid phase. This shif tting requires certain pressures and temperatures that meet the temperature and pressure boundary to become li q uid. This study performs a simulation to determine the pressure and temperature constraints required to convert CO 2 from the gas phase into the li q uid phase. This study uses a finger-type slug catcher mounted on the seabed. The finger - type slug catcher co nsists of parallel pipes of a certain diameter and length. Furthermore, the simulation is done by variations of several variables , such as: inlet pressure, ambient temperature, inner pipe diameter, and number of branches. The aim of the research is to design a slug catcher model so that the separation of methane and CO 2 gas can occur optimally. The slug catcher design resulting from this study includes the value of the inlet pressure, diameter, and minimum length of the pipe where CO 2 begin to form li q uid. The simulation was done by 320 kinds of combination for the range of inlet pressure value from 800 to 1500 psia, the range of pipe inner diameter is 40 - 50 inch, the range of ambient temper a ture is 50 - 80 o F, and the range of 1 to 5 number of branches. Based on the simulation, when the inlet rate of 200 MMSCFD, the inlet temperature of 100 o F and the overall heat transfer coefficient of 200 BTU / hr / ft 2 / o F , it is obtained the shortest length of slug catcher is 72.18 ft, 40-inch inner diameter, and the inlet pressure requirement is 1000 psia. Furthermore, for design purposes, the slug catcher length is made 217 ft to ensure that the li q uid CO 2 formed at 72.18 ft from the inlet and the liquid can accumulate and keep flowing towards the end of the pipeline for further utilization.

A combined kriging and stochastic method to map paraffin scale growth in oil pipeline

Paraffin is a common deposit in oil production pipeline. It occurs when the oil flowing-temperature is under Wax Appearance Temperature (WAT) or pour-point temperature. Several prediction models so far only estimatethe location where the paraffin-wax is possibly formed and there is no prediction about paraffin-wax growth over time. Therefore, this paper presents a new mathematical model to accurately predict paraffin-wax growth in oil production pipeline. The proposed model contains stochastic and kriging method. The stochastic model is developed based on Markov and Poisson model and used to describe the generation time and growth of scale. Kriging model is then combined to describe the position of scale along the production pipeline. As the result of the combined model, paraffin-wax thickness can be mapped in space and time. This prediction is important to determine and decide an effective production operation and efficient investment.