Scale formation in downhole tubular-flow passages can cause partial to complete plugging that will affect production or injection rates adversely. In an intelligent-well completion in which the interval-control-valve (ICV) opening must be changed to control flow rate, the completion will become ineffective if plugging of clearances prevents valve actuation. To mitigate these problems, a method to predict the potential rate of scale formation under realistic conditions has been developed.This empirical method allows prediction of tool performance under scale-forming conditions for downhole applications, and uses chemical data and flow fields generated by computationalfluid-dynamics (CFD) models for downhole tools. Chemical data are obtained from laboratory tests on coupons by use of brines matching the chemistry of connate fluids. Tests were conducted in a high-pressure, corrosion-resistant vessel over a range of high pressures (100 to 10,000 psi) and high temperatures (75 to 150 C) to simulate downhole well conditions. Two test sets were conducted, each with fluid at rest and with an impeller generating low velocity in the reaction vessel, ranging from 4 hours to 4 days, with scaling rates determined from coupon-weight gain. Concentrations in the range of 50 to 125% of the typical connatefluid concentration were used.The weight-gain data obtained from the coupon tests and from a tube-plugging test were used to develop an empirical model for scale-growth rate at a given point on a solid surface with pressure, pressure gradient, temperature, fluid velocity, and brine concentration as independent variables. Artificial-intelligence methodology was used to develop this model, which can be used to predict the scale-growth rate for any arbitrary geometry. By use of the internal geometry of any tool to be modeled, a CFD model is prepared and the pressure, fluid velocity, and pressure-gradient data are generated for the entire internal solid surface of the tool for a given flow rate through the tool. These data are fed into the empirical model to calculate the scale-growth rate, which is integrated to obtain scale thickness at each point of the internal solid boundary. To verify accuracy, scale formation in a 4.5-in. ICV was predicted at high-pressure, high-temperature conditions at a low flow rate. Laboratory tests on the valve matched the model predictions well enough, which enabled Petrobras to design a better completion and fluid-handling system for a presalt well. BackgroundInorganic-scale formation associated with brine solutions from oil and gas wells has been a major issue, leading to production restrictions and costly downtime to remove the scale. In the past few decades, there have been a significant number of studies conducted to understand the mechanism of scaling at elevated temperatures and pressures that correspond to well operating conditions and to develop models to predict the change in scaling

Scale formation in downhole tubular-flow passages can cause partial to complete plugging that will affect production or injection rates adversely. In an intelligent well completion in which the interval control valve (ICV) positions must be changed in order to control flow rate; the completion will become ineffective, if plugging of clearances prevents valve actuation. To mitigate these problems, a method to predict the potential rate of scale formation under realistic conditions has been developed. This paper describes this method, which allows prediction of tool performance under scale-forming conditions for downhole applications. This semi-empirical method uses chemical data and flow fields generated by computational fluid dynamics (CFD) models for downhole tools. Chemical data are obtained from laboratory tests on coupons using brines matching the chemistry of connate fluids. Tests in a high-pressure, corrosion-resistant vessel over a range of high pressures (100 to 10,000 psi) and high temperatures (75 to 150°C) to simulate downhole well conditions have been conducted. Two test sets each with fluid at rest and where an impeller generates low velocity in the reaction vessel were conducted, ranging from 4 hours to 4 days with scaling rates determined from coupon weight gain. Concentrations in the range of 50% to 125% of the typical connate fluid concentration were used. The laboratory test data are used with velocity field data to develop an artificial-intelligence-based mathematical model to determine scale formation rates. The model can be applied to any tool geometry as long as the operating conditions are within allowable limits of the model. The model also provides some insight into the mechanism of scale formation. To verify accuracy, scale formation in a 4.5-inch interval control valve was predicted at high-pressure, high-temperature conditions at a low flow rate. Laboratory tests on the valve matched the model predictions reasonably well, enabling Petrobras to design a better completion and fluid-handling system for a pre-salt well.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractA well equipped with downhole flow control and monitoring devices provide tools to manage reservoir and well performance. Many optimization techniques have been applied in conjunction with commercial reservoir simulators with detailed multisegment well model to improve ultimate recovery. It is important to know that all the benefits of the reservoir optimization can be lost if the availability and appropriate control of the artificial lift is not used. A well equipped with three on-off type flow control valves and four pressure and temperature optical transducer: one for each zone plus one more above the production packer in combination with a rod-pump control with moderate service condition specifications is described. Standardization of the connectivity between automation of the intelligent completion and artificial lift is necessary for a vendor-independent integration, which becomes more costeffective by using open industry standard protocols. This also allows the use of different supervisory software, reducing the impact of changing systems already in use without losing the flexibility of choosing a solution that has some custom features or that are cheaper. This paper will discuss both the benefits resulting of the integration between intelligent completion and artificial lift automation. The value added to provide better decision making to respond immediately to unexpected changes, the need of satisfy reliability demands of the operational people and the issues involving the use of intelligent completion in mature fields.

Scale formation in downhole tubular-flow passages can cause partial to complete plugging that will affect production or injection rates adversely. In an intelligent well completion in which the interval control valve (ICV) positions must be changed in order to control flow rate; the completion will become ineffective, if plugging of clearances prevents valve actuation. To mitigate these problems, a method to predict the potential rate of scale formation under realistic conditions has been developed. This paper describes this method, which allows prediction of tool performance under scale-forming conditions for downhole applications. This semi-empirical method uses chemical data and flow fields generated by computational fluid dynamics (CFD) models for downhole tools. Chemical data are obtained from laboratory tests on coupons using brines matching the chemistry of connate fluids. Tests in a high-pressure, corrosion-resistant vessel over a range of high pressures (100 to 10,000 psi) and high temperatures (75 to 150°C) to simulate downhole well conditions have been conducted. Two test sets each with fluid at rest and where an impeller generates low velocity in the reaction vessel were conducted, ranging from 4 hours to 4 days with scaling rates determined from coupon weight gain. Concentrations in the range of 50% to 125% of the typical connate fluid concentration were used. The laboratory test data are used with velocity field data to develop an artificial-intelligence-based mathematical model to determine scale formation rates. The model can be applied to any tool geometry as long as the operating conditions are within allowable limits of the model. The model also provides some insight into the mechanism of scale formation. To verify accuracy, scale formation in a 4.5-inch interval control valve was predicted at high-pressure, high-temperature conditions at a low flow rate. Laboratory tests on the valve matched the model predictions reasonably well, enabling Petrobras to design a better completion and fluid-handling system for a pre-salt well.

A well equipped with downhole flow control and monitoring devices provide tools to manage reservoir and well performance. Many optimization techniques have been applied in conjunction with commercial reservoir simulators with detailed multisegment well model to improve ultimate recovery. It is important to know that all the benefits of the reservoir optimization can be lost if the availability and appropriate control of the artificial lift is not used. A well equipped with three on-off type flow control valves and four pressure and temperature optical transducer: one for each zone plus one more above the production packer in combination with a rod-pump control with moderate service condition specifications is described. Standardization of the connectivity between automation of the intelligent completion and artificial lift is necessary for a vendor-independent integration, which becomes more cost-effective by using open industry standard protocols. This also allows the use of different supervisory software, reducing the impact of changing systems already in use without losing the flexibility of choosing a solution that has some custom features or that are cheaper. This paper will discuss both the benefits resulting of the integration between intelligent completion and artificial lift automation. The value added to provide better decision making to respond immediately to unexpected changes, the need of satisfy reliability demands of the operational people and the issues involving the use of intelligent completion in mature fields. Introduction Carmópolis field, a mature field in the northeast region of Brazil is a land field discovered in 1963 and since 1971 uses waterflooding as its main improved oil recovery (IOR) method. A review of the waterflooding operation through improved reservoir characterization and flow simulation, as well as the investigation of other IOR methods were done as part of PRAVAP (Petrobras Strategic IOR Program) and it was proved that it is possible to reverse the declining production trend of the field using waterflooding management as well as selectivity.[1] The results have addressed the quantification of potential gains from intelligent completion technology. Petrobras has also chosen Carmópolis field as its first intelligent field pilot project[2] aiming to get better results on reservoir and production management. An integrated intelligent well system was developed for the wells in the pilot. In this system the artificial lift automation is completely integrated with the intelligent completion to satisfy availability and reliability demands of the operational people and ensure that the benefits of the reservoir and production optimization will be effective. The well completion chosen was three hydraulic/hydrostatic packers, three flow control valves and four pressure and temperature optical fiber sensors. The equipments were developed to be low cost with moderate service condition specifications. The artificial lift method used in Carmópolis is rod pump and the regular practices are to locate the pump below perforation and to use fixed pumps. A complete new automation system was used to integrate all systems using open standards and remote diagnostics ability. The initial results showed better reliability, diagnostics and capex (capital expenditure) when compared with similar implementations in Petrobras. The expectation from now on is to get better opex (operational expenditure) also.