Petroleum Experts' Integrated Production Model (IPM) suite of software is widely used in the E&P industry especially for project evaluations that require integration of both surface and subsurface models. There is evidence in the literature to show diverse applications in field development planning, integrated forecasting, surveillance and production system optimization. Perhaps less reported are the lessons learned and best practices in using the IPM software. This paper focuses on these issues using Chevron's IPM model for some of its largest gas fields.The Non-Operated Joint Venture (NOJV) Subsurface Team began developing an IPM model for one of its biggest gas assets in 2005. With explicit modeling of critical components like compressors, dozens of wells and reservoir tanks, platforms, fluid characterisation, gas-water contact movement, pipelines, sub-sea manifolds and separators, this is arguably the largest and most complex IPM model in Chevron. The model continues to play a critical role in Chevron's effective capital stewardship of the gas asset. The need to maintain the credibility of this model cannot be over-emphasized, and the model has undergone several phases of enhancement to ensure that it continues to meet business objectives.This paper describes some of the best practices and lessons learned in constructing and maintaining a complex IPM model. It is intended as a resource for IPM practitioners. Examples cover all aspects of the IPM from the non-technical (e.g. framing the problem, case definition and naming convention) to the technical (e.g. model construction, model maintenance, software limitations, constraint violations, production optimisation and quality assurance checks). IntroductionProduction forecasting involves attaching a timescale to production recovery and it is one of the most vital roles of reservoir engineering. It underpins the cashflow of any project and can make the difference between a project being sanctioned or abandoned. The complexity of the role is underscored by the requirement to integrate multiple and diverse disciplines including subsurface characterisation, surface network configuration, production philosophy, economic limits, business decisions and operational constraints.Unlike production forecasting for oil fields, gas forecasting is further complicated by long-term contracts and the need to meet contractual obligations. This requirement means that gas companies need to correctly predict the execution of future projects to ensure that they have enough gas to satisfy their contractual oligations. Usually, in gas forecasting, multiple fields with diverse fluid properties are produced simultaneously and this further introduces the complication of gas quality whilst also maximizing the value of by-products like condensate and natural gas liquids. These complexities indicate that an integrated gas forecasting model is required to accurately predict production for a gas field. There are many such products available including company proprietary software for internal us...
In this work, we develop a new analytical solution for the problem of deep bed filtration under size-exclusion dominated particle capture. An uspcaled stochastic micro model has been derived that models individual particle-pore interactions. The solution is based upon mono-dispersed suspension injection into a medium with a known pore throat distribution. Ultimately, the model can be used for matching with laboratory corefloods, simulation of permeability decline or expected tracer breakthrough. Size-exclusion particle capture during suspension transport in porous media takes place during drilling fluid penetration into oil bearing formations, sand production and the injection of poorly treated water. Straining particle capture can dominate over the particle attraction in cases of produced water injection, injection of produced water from aquifers, fines production in fractured wells and gravel packed horizontal wells. Deep bed filtration contributes highly to the problem of formation damage. Planning and design of the above-mentioned oil production processes is based on reliable mathematical modeling. If compared with previous population balance models for the general case of particle capture, the current work discusses a particular case of pure straining. We show that in the case of particle-grain and particle-particle repulsion, the particles cannot be strained in pores larger than the particle size. The particles can be captured in thin pores only. These considerations change the expression for capture rate in the basic population balance model. In order to validate the proposed model by comparison with laboratory tests, we found a travelling wave solution. The comparison with 2 sets of laboratory data showed good agreement, which validated the proposed model. The new solution has broad application to the petroleum industry. The developed model can be applied for: (1) Particle sizing for drilling fluids, (2) Log interpretation of filtrate penetration into the formation, (3) Design of injected water treatment and (4) The design of gravel packs to minimise fines production and formation damage.
Summary Presented here is an analytical framework to assess the impact of transient-temperature changes in the wellbore on the pressure-transient response of cold-water injection wells. We focus attention on both drawdown and falloff periods in a well after injection. Historically, these pressure data have been used to calculate reservoir properties concerning flood-efficiency and completion properties (formation permeability/thickness, mechanical skin, and fluid-bank mobilities). One key question addressed in this paper is whether the effects of thermal heating of wellbore fluids during a falloff survey can mask the pressure signature of a two-region composite reservoir. The pressure deflections required to detect mobility changes can be relatively small compared with pressure changes induced by temperature effects in the well. The framework proposed in this paper allows for the numerical evaluation of the contribution of each. Previously, researchers have studied multiple bank-transient-injection problems extensively for the case of reservoir flow and pressure drop, even for nonisothermal problems. The effect of temperature changes in the wellbore and overburden are seldom discussed, however. It is demonstrated in this paper that these effects can, in some cases, be substantial, and it is worthwhile to incorporate them into an interpretation model. The results of this paper are useful for planning and designing a pressure-falloff survey to minimize the adverse effect that heating of wellbore fluid by overburden rock can have on the pressure-transient signature. The theory can also be used to analyze existing data affected by the phenomenon. A real-field case study is shown for a cold-water injector where pressure-falloff data have been affected by temperature changes. The analytical model fits the field data closely when parameters are adjusted within reservoir-property-uncertaintyranges.
Summary Barrow Island (BWI), 56 km from the coast of Western Australia (WA), is home to several mature reservoirs that have produced oil since 1965. The main reservoir is the Windalia Sandstone, and it has been waterflooded since 1967, whereas all the other reservoirs are under primary depletion. Because of the maturity of the asset, it is economically critical to continue to maximize oil-production rates from the 430 online, artificially lifted wells. It is not an easy task to rank well-stimulation opportunities and streamline their execution. To this end, the BWI Subsurface Team applied the Lean Sigma processes to identify opportunities, increase efficiency, and reduce waste relating to well stimulation and well-performance improvement. The Lean Sigma methodology is a combination of Lean Production and Six Sigma, which are methods used to minimize waste and reduce variability, respectively. The methods are used globally in many industries, especially those involved in manufacturing. In this asset, we applied the processes specifically to well-performance improvement through stimulation and other means. The team broadly focused on categorizing opportunities in both production and injection wells and ranking them—specifically, descaling wells, matrix acidizing, sucker-rod optimization, reperforating, and proactive workovers. The process for performing each type of job was mapped, and bottlenecks in each process were isolated. Upon entering the “control” phase, several opportunities had been identified and put in place. Substantial improvements were made to the procurement, logistics, and storage of hydrochloric acid (HCl) and associated additives, enabling quicker execution of stimulation work. A new program was also developed to stimulate wells that had recently failed and were already awaiting workover (AWO), which reduced costs. A database containing the stimulation opportunities available at each individual well assisted with this process. The project resulted in the stimulation of several wells in the asset, with sizable oil-rate increases in each. This case study will extend the information available within the oil-industry literature regarding the application of Lean Sigma to producing assets. It will assist other operators when evaluating well-stimulation opportunities in their fields. Technical information will be shared regarding feasibility studies (laboratory-compatibility work and well-transient-testing results) for acid stimulation and steps that can be taken to streamline the execution of such work. Some insights will also be shared regarding the most-efficient manner to plan rig work regarding stimulation workovers.
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