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Abstract This paper describes the stress field and rock mechanical aspects in the Gullfaks Field reservoirs. The data analyses are mainly based on large volume water and gel "mini-fracs" associated with data gathering in conjunction with propped fracture stimulation jobs. A few data points represent results from Pump-In/Decline Tests after perforating/before start-up of single zone water injectors. The data are based on "state of the art" mini-frac analysis techniques which also are discussed in this paper. The tests have been carried out over a 20 year period with varying or cycled reservoir pressure. The systematized data from 52 mini-frac tests executed in sandstone with high porosity, high permeability and very low effective stresses give results that cohere with linear elastic rock mechanical theory. The connection between faults, tectonics and local stress is discussed. The data represent a unique gathering of information which has supplemented to the understanding of the overall geological structural picture. Further, this paper discusses how the analysis of data founded a revised approach to solve drilling challenges as a result of small drilling margins and localized depletion. Also, this paper presents a new graphical solution illustrating the depletion dependant stress change versus reservoir configuration or structural boundaries. Introduction The Gullfaks Field is located in the central part of the East Shetland Basin in the Northern North Sea. The Statoil Hydro operated field has been developed with 3 Condeep platforms and started production (Phase-1 Development from GFA and GFB) in December 1986. Production from GFC (Phase-2 Development) commenced in Janauary 1990. A total of 188 development wells (including all side tracks and 6 subsea wells) have been drilled as of December 2007. Field peak production was exceeding 95,000 Sm3/d (600,000 bopd) in 1994. Current production averages 16,000 Sm3/d (100,000 bopd). Predicted ultimate oil production from the field is more than 358 Mill Sm3(2,252 Mill bbl), of which 335Mill Sm3(2,107Mill bbl)had been produced by January 1st 2008. Forecasted ultimate oil production from the field represents 62 percent field recovery. The main drive mechanism is water injection with some scattered and intermittent gas injection in mainly downflank WAG injectors. The oil is mainly located within three major sandstone units, the Middle Jurassic Brent Group and the Early Jurassic Statfjord and Cook Formations, containing approximately 75 percent, 16,5 percent and 7,5 percent, respectively, of the mapped hydrocarbon pore volume (STOOIP) of 582 million Sm3(3,660 million bbl) in place. The Lower Brent delta sequence is comprised of the Broom/Rannoch, Etive and Ness Formations, and the Upper Brent consists of the Tarbert Formation. Broom/Rannoch/Etive, Ness and Tarbert are considered as three separate reservoirs with regard to field development. The reservoirs are highly overpressured, with a representative initial reservoir pressure of 310 bar (4,495 psi) at datum depth (1850 m (6070 ft) below mean sea level). Reservoir temperature is 72 deg. C (162 deg. F) at datum level. The shallow, highly porous and permeable sands, consisting of mainly quartz, feldspars together with autogenious kaolinite, mica and calcite filling, are generally poorly consolidated. Net-pay porosity and permeability vary between 28–38 percent and 2.0 - 10,000 md, respectively. The oil is undersaturated, with a typical saturation pressure of approximately 245 bar (3550 psi), depending on formation, depth and location. Production wells in the Rannoch and Cook Formation are mainly IVFC (indirect vertical fracture completion) wells. These wells are propped fracture stimulated through perforations/completions in lower stratigraphical zones with lower permeability and higher sand strength which communicate with overlying high permeability and weak zones through the propped fracture1, 2. The majority of stress test data published in this paper results from mini frac analyses in conjunction with these stimulation jobs.
The Khuff and Pre-Khuff are deep gas condensate reservoirs under active tectonic stress environment. The reservoirs are under development using horizontal wells and vertical wells with hydraulic fracturing. Modeling geomechanical rock properties accurately is essential for ensuring a successful frac job design and execution. During the last two years, a large amount of additional lab and field information has become available. Integration of all the data was conducted for better estimation of in-situ geomechanical rock properties.
This paper presents the results of a mathematical algorithm for calculating the geomechanical rock properties for the Khuff and Pre-Khuff reservoirs in the Ghawar field. The model is derived from the classical poroelastic model in addition to a tectonic strain component as proposed by Prats and Warpinski. The model was calibrated to lab data as well as to the results of several Microfrac and Minifrac field tests. The model was further improved by calibrating it with actual history-matched frac data.
The algorithm describes a methodology for systematically calculating geomechanical rock properties and in-situ minimum horizontal stress magnitude from sonic shear and compression log data. The paper also describes a detailed history-matching algorithm for Minifrac and frac data using a 3-D frac simulator. The results show that the minimum in-situ stress in the Khuff and Pre-Khuff reservoirs is governed by the tectonic effect, which is Young's modulus dependent. Detailed analysis and well examples are presented.
Well stimulation technology has proven to be successful in improving hydrocarbon recovery.1 Many wells are stimulated to increase productivity and recovery. Two types of well stimulation techniques are generally adopted, viz., hydraulic fracturing and acid fracturing. The first type is used in sandstone reservoirs and high-conductivity proppants are used to keep open the fracture initiated and propagated mainly by the pad fluid pumped prior to proppants. The second type is used for carbonate reservoirs where acid is used to react with the rock once a fracture is created by the viscous pad. The reaction of the acid etches the fracture walls and matrix rock creating a conductive path from the reservoir to the wellbore.
Saudi ARAMCO has initiated an acid fracturing program to treat the Khuff carbonates and Pre-Khuff sandstone reservoirs in the Ghawar field in the eastern province of Saudi Arabia. The fracture treatments conducted thus far have resulted in very encouraging gas rate and well productivity. In this paper, we discuss some of the main reservoir properties that impact fracture and production behavior, which are the geomechanical properties. We will provide a review of the mathematical models used to generate the data. We will also provide a systematic approach for calibrating and improving the model by integrating and history matching field data. Actual field examples will be provided to illustrate the process.
Both reservoir properties, particularly the mechanical properties, and perforation placements dictate the geometry of the fracture and its effectiveness. Placement of perforations is controllable and should be based on accurate prediction of reservoir flow and geomechanical properties. Therefore, it becomes very important to accurately predict geomechanical properties.
The focus of this paper is on the Khuff and Pre-Khuff Jauf reservoirs in Ghawar field. The structure map is presented in Fig. 1.
Improved fracturing treatment design has been achieved by computerised interpretation of the mechanical properties logs, laboratory rock properties measurements and production logging during minifrac tests. properties measurements and production logging during minifrac tests. By combining these data acquisition techniques in a single well, optimum hydraulic fracturing design input information is obtained.
These techniques have been successfully used in a Southern Sector North Sea well. Subsequently, real-time and post-treatment evaluations have indicated the reliability of the technique.
Actual field data are presented showing use of these data. Also presented is a computer generated fracture geometry processed from mechanical properties log data. Due to the importance of accurate fracture geometry properties log data. Due to the importance of accurate fracture geometry prediction in current North Sea gas field development, the techniques have prediction in current North Sea gas field development, the techniques have proven to be a valuable aid. proven to be a valuable aid
Hydraulic fracturing treatments have increased in the North Sea, particularly in the Southern Sector gas fields. Operators are predicting particularly in the Southern Sector gas fields. Operators are predicting that future field development will also require fracturing, but hydraulic fracturing is expensive and must be designed properly to be effective.
Hydraulic fracture-design research generally concludes that fracture height and fracture width are the main parameters influencing the design of a hydraulic fracturing treatment. Both fracture height and fracture width are highly dependent on the minimum stress at each point in the formation and on fluid loss through the fracture faces.
In the past, most fracture design models have assumed a fracture height and then calculated length and width. Today, several pseudo three- dimensional fracture models care available but they are often underutilized through a back of accurate fracture-height input data. By making available a continuous stress profile across the zones of interest a better estimate of fracture height can be calculated using these models.
Three approaches to designing more accurate fracture geometry are utilised:
i) Determine fracture height from a model which computes the fracture migration using the continuous fracture pressure gradient from a mechanical properties log.
Summary When the first frac pack was performed in the Gulf of Mexico (GOM) over a decade ago, very little was known about the effects this form of sand control would have on the intended formation. Even less was known about how to optimize the treatment to obtain the most benefit for the formation. Since that time, the sand control community has learned a great deal about the effects and benefits of frac-packing various unconsolidated formations throughout the world. However, most of the knowledge and design criteria have remained housed within the minds of individuals and cannot be looked at as a whole to find trends and fine-tune the design methods currently being used. Another complicating factor is the number of frac models of varying degrees of complexity being used within the industry. Therefore, even though thousands of frac packs have been performed globally, frac-pack redesign methods are still subjective and differ from individual to individual and from model to model. The recent creation of a database that houses selected formation evaluation test (FET) and frac data along with model-specific parameters allows full-scale analysis of a large number of jobs pumped in the Gulf of Mexico (GOM). With a consistent analysis procedure in place, the database, populated with numerous treatments by engineers working throughout the GOM, can be analyzed objectively. The data contained in this database include rock mechanics, net pressures, pumping trend data, tip screenout (TSO) times, and other variables. This paper explains the methodology and discusses the results of the database analysis, using case studies to determine the best method for analysis of the jobs. Crossplots show the correlation between TSO prediction and actual events and suggest recommendations for more successful design work in the future. This paper is meant to give up-to-date guidelines for designing better frac packs. Introduction Within the sand control community, the ability of an engineer to redesign a frac pack from data generated during the minifrac can sometimes be considered more art than science. Often the engineer whose job it is to formulate the frac-pack treatment will use several different methods to arrive at a solution deemed most correct. Many hours are often dedicated to determining the proper design for a frac-pack treatment. While often the results cannot be argued with, it is unwise and possibly a waste of time to reinvent the wheel for every job. Rather, it should be the goal of the sand control and frac-pack communities to develop a design method that can determine the most important parameters necessary to complete the job. This method would be easily repeatable and could be used throughout the GOM and possibly in other high-permeability, unconsolidated regions of the world. The purpose of this paper is to solve the problem previously described. The goal of the project was to predict TSO events accurately so that the fracture geometry could be better understood. In fracture theory, the TSO (Ellis 1998) is the point at which there is no longer any propagation of the fracture length. Most fracture models treat this as the time at which the first grain of sand is exposed to the tip of the fracture, impeding any further growth and allowing net pressure to accumulate and build width throughout the length of the fracture. The creation of a reliable standard method for predicting the onset of a TSO event would enable engineers involved in designing frac-pack operations to become more uniform in their procedures and provide more accurate results. Knowing which "knobs" within the fracture model should be manipulated in order to obtain the most accurate results would be invaluable. This would allow for much quicker analysis of the data accumulated from the minifrac, thereby saving expensive rig time. In addition, should multiple engineers be analyzing the data, a single reliable method would allow a much clearer resolution of any issues that arise, because everyone should see very similar results. Many efforts have been made to accomplish this (Dusterhoft et al. 1995). This paper presents a methodology for developing a database system to track frac-pack treatment data, identifying the data deemed necessary to build a reliable model, and procuring that information from various fracture treatments. Case studies are then presented which prove that the model, populated according to the recommended procedure, accurately predicts the TSO event in various situations.