In today's competitive market it is increasingly important to improve production and increase reserves in the most efficient way possible. One alternative is to add new wells, but this can be an expensive and time-intensive endeavor. Another alternative is adding and optimizing production from the thousands of existing wellbores already in place. To do this, the ability to detect and evaluate bypassed hydrocarbons and track fluid movement in the reservoir is vital. Current methods of through-casing saturation determination using nuclear tools are limited because of their shallow depth of investigation.
With the introduction of the CHFR* cased hole formation resistivity technology a new dimension has been added to cased hole evaluation. A deep-reading formation resistivity can now be obtained through steel casing. Although this measurement is analogous to exsisting techniques for saturation determination, it has two distinct advantages:
In many cases there is a need to monitor saturation changes in a producing interval. In the past sigma-based interpretation methods were the only option. However, saturation determination in low-porosity reservoirs (<15 pu) using sigma requires a very detailed and accurate lithology description because small errors in the lithology answer have a significant effect on the saturation from sigma. Because many reservoirs in the Permian Basin combine complex lithology with low porosity, answers can be somewhat unreliable. This is often the case in shallow water-flooded carbonates, where the presence of gypsum, anhydrite and salt makes sigma measurements almost impossible to interpret.
Cased hole saturation is also needed to detect bypassed pay and occasionally as the primary evaluation method. In these cases, sigma-based measurements fail because of their shallow depths of investigation. Because the zones have not been produced yet, the invading fluid (brine) still occupies the pores around the borehole. This is particularly true in the Permian Basin because no fluid control measures are used during drilling and deep invasion is normal. The CHFR tool reads deep enough to avoid this complication. In fact, it is commonly superior to openhole resistivity since it reads significantly deeper than openhole resistivity tools.
When the CHFR tool is used in conjunction with porosity and lithology tools, run either in openhole or through casing, accurate hydrocarbon saturation can be determined. This methodology can be used to find bypassed pay or to determine the depletion level of producing pay intervals. It has also been used extensively in time-lapse surveys to evaluate the sweep efficiency of secondary recovery, ensuring that no producible hydrocarbons are left behind. This paper reviews the application of this technology in several U.S. basins, including the Permian Basin and California.
Ever since Conrad and Marcel Schlumberger first began measuring formation resistivity in 1927, scientists have been working to evolve the measurement to work in all types of conditions. Arguably the most challenging has been adapting the measurement to operate in a cased hole environment. While the measurement principle is quite simple, making it through conductive steel pipe requires highly advanced instrumentation. Potential differences in the nanovolt range must be measured accurately to make the measurement of formation resistivity possible. The CHFR tool provides this technology.
Porosity devices such as neutron and sonic tools have been characterized for cased holes for years. However, the ability to calculate water saturation that enables direct comparison to openhole evaluations using standard equations requires the formation resistivity. The CHFR tool can provide that missing piece of the puzzle. Sigma-based interpretation techniques have been tried but the shallow depth of investigation places the measurement in the highly invaded zone. This method of interpretation in cased holes is also hindered by lithology variations that are typical in the Permian Basin.
San Andres carbonate reservoirs have long been known to have a high degree of reservoir heterogeneity and poor recovery efficiencies. Fractures are one of several causes of this heterogeneity. The heterogeneity causes unpredictability in water and CO2 flooding. However, the correct placement of horizontal wells can take advantage of this problem.
An integrated reservoir characterization study of the Mabee field incorporating oriented core, Formation Microscanner (FMS) wireline logs, seismic time slices, production character, curvature analysis, and interference testing was used to predict fracture orientation and areas of highest fracture density. These fracture characteristics were then applied to determine horizontal well loca-tion and orientation. Fracture orientation was evaluated through the analysis of oriented core, FMS logs, and interference testing, indicating a fracture orientation of N70W. Analysis of the induced fractures in the oriented core indicates that the direction of maxi-mum horizontal compressive stress is N45E. High fracture density was delineated by curvature analysis, relative seismic amplitude, and areas of higher production. Areas with high curvature corre-spond to areas of high relative seismic amplitude and higher production. The data integration indicates that four areas have high fracture density. The synthesis of fracture orientation and density, along with the production character, indicates the optimal location and orientation of horizontal wells.
Low-permeability San Andres reservoirs of the Central Basin Platform contain significant volumes of remaining oil. The Mabee San Andres field lies on the northeastern edge of the Central Basin Platform (Fig. 1) and is part of the San Andres/Grayburg Platform Carbonate play.1 Ref. 1 reported recovery efficiencies for secondary recovery of approximately 30% and an unrecovered resource of 2.6 billion stock-tank barrels of oil. The low recovery efficiency and still-remaining resource are due largely to the signif-icant amount of heterogeneity found in these reservoirs.
San Andres Platform Carbonate reservoirs are highly hetero-geneous because of the depositional facies, diagenesis, and frac-turing. Ref. 2 described how grainstone bar depositional facies significantly affected the production character in Dune (Grayburg) reservoirs. Ref. 3 described how areas of postdepositional dia-genesis were the most highly productive in the Jordan (San Andres) reservoir. Additionally, fractures have been cited as contributing significant heterogeneity to San Andres/Grayburg reservoirs. Ref. 4 sited fractures in the Arrowhead (Grayburg) reservoir as the reason that tracers broke through in 2 days between a five-spot well pat-tern. Ref. 5 described the influence of fractures in the Keystone East (San Andres) reservoir. Ref. 6 described how fractures in the Chaveroo and Cato (San Andres) reservoirs influenced flow and storage volume. Ref. 7 depicted natural fractures as dominating the permeability character in zones of the Levelland (San Andres) reservoir.
This heterogeneity causes preferential fluid flow and often-early breakthrough in waterfloods. It is also the likely cause of water loss previously unaccounted for in San Andres waterflood operations. Ref. 5 described a northeast preferential flow direction coincident with their interpreted direction of maximum horizontal compressive stress. Ref. 8 cited the Fullerton Clear Fork, Keystone Colby, and Means (San Andres/Grayburg) reservoirs as having east-west preferential flow directions. It is reasonable that this similar preferential flow direction in several fields and several formations is due to open fractures.
Both the direction of open fractures and the location of densely spaced fractures influence how fractures affect production. In this study we combine geologic and engineering information including interference tests, oriented core, Formation Microscanner (FMS) logs, production data and curvature analysis to evaluate the direc-tion of open fractures and the areas where they may be more densely spaced.
The cessation of CO2 injection may be the result of poorreservoir response, low oil prices, or near the end of a CO2project. The focus of this paper is to describe the effect ofCO2 injection curtailment on oil recovery and production in atechnically successful CO2 flood of an oil reservoir. TheCO2 curtailment is sustained over a significant period of time whilewater is injected. Following the curtailment, CO2 is againinjected.
Early EOR literature covers CO2 and miscible gas injection in thepresence of very high water saturation. The key issues are phasebehavior, surface tension, viscosity, and oil stripping, which are described interms of curtailment and the subsequent affect on oil production.
A survey of pertinent literature has been conducted to infer the impactCO2 curtailment has on reservoir performance. Preliminaryfindings are that the curtailment of CO2 leads to a permanent lossin reserves and production rate. Scenarios that may alleviate theselosses such as reduced CO2 injection instead of complete curtailmentare addressed.
A CO2 flood may become uneconomic due to high operation costs orlow oil prices. Due to the expense of purchasing CO2 forinjection, CO2 purchases may be halted until a more favorableeconomic situation exists. The effects of curtailing CO2injection, replacing it with water injection for a significant period of time,and then starting CO2 at a later time is the subject of thispaper.
An analogy to curtailment is injecting CO2 at initially highwater saturations, which has been studied extensively in the literature due toearly concerns of the effectiveness of CO2 to mobilize waterfloodresidual oil. The key issues are wettability, solubility ofCO2 in water, phase behavior (miscibility), diffusion, dispersion(viscous fingers), and oil bank formation.
In an attempt to understand the technical aspects of CO2curtailment, field case publications were intentionally not included.While this work has not been previously published, it was first presented atthe 1999 CO2 Conference in Midland.
Determining the water saturations in thin-bedded turbidites using wire-line logs is difficult; errors in Sw calculation frequently result in uneconomical completions. Consequently, current Brushy Canyon completion decisions include expensive core information to provide an acceptable indicator of oil saturation in order to compensate for the Sw calculation problem. Completion decisions can be improved and less core data is needed using a new method that correlates wire-line logs with core measured bulk volume oil (FSo).
A neural network was trained and tested using density and neutron porosity plus shallow and deep resistivity logs as input variables. The neural network was trained to predict the FSo product from whole core analysis.
The trained and tested neural network was then used to estimate FSo in 25 additional Brushy Canyon wells that were not used in the training, but had the same four wire-line logs. A FSo cutoff of 22 units was determined and values greater than the cutoff were summed through the perforated interval in each well. The summed bulk volume oil of the 25 wells was plotted versus the first year's total production. The plot suggests that SFSo greater than 20,000 units will usually result in an economical new well or reentry completion.
During the course of optimizing the neural network architecture, valuable insights into network architecture design were gained. For this type of study, less complex architectures produced robust testing results, indicating that the solution, though non-linear, is still reasonably simple.
The method should be useful when evaluating behind-pipe completion opportunities in the Brushy Canyon interval of the Delaware Sands in the Permian Basin. Re-completion costs are lower than new well costs; thus thin zones with high values of FSo are potential targets.
The Delaware Mountain Group in the Delaware basin of New Mexico consists of a thick (4500 ft) sandstone and siltstone interval with 95% of the sandstone medium to fine-grained.1 Porosity and permeability in the productive interval range from 12-25% and 1-5md respectively.1 Typically the clay content is less than 5%.1 Stratigraphic divisions are uncertain,1 but the top of the Lower Brushy Canyon is regionally identified by a kick in the gamma ray and the accompanying resistivity logs. A standard suite of logs includes gamma ray, neutron and density porosity, plus shallow and deep resistivity. Generally the density log produces the best estimate of porosity, but calculating water saturation is problematic.1 Others2,3 have reported similar problems in estimating water saturation in thin-bed, low resistivity formations.
Around 1990 improved sidewall coring technology resulted in the recovery of samples for laboratory analyses and the ability to accurately record sample depth. Reference 2 recognized the thin-bed, low resistivity problem and developed a procedure to calibrate the available logs with the new core information. The procedure follows:
"using the full-core analysis to calibrate log calculations, a procedure was developed to identify the zones that are oil-productive. The procedure is based on the premise that zones with residual oil saturation have a high probability of being productive and zones with no residual oil saturation have a low probability of being productive. By calibrating the Micro Lateral Log to calculate a residual oil saturation value for each one-half foot interval from the digitized log, potential pay zones were identified. By applying porosity correction transforms, setting gamma ray and porosity limits, and calibration of resistivity values, a more accurate determination of the productive intervals was made."
Reference 3 recommends accounting for the difference in scale between the point measurements of the core analysis and the lower resolution log measurements by including (adjust log parameters, "m" and "n") the location of each plug in the log interpretation. Both Refs. 2 and 3 are methods of calibrating well-known equations with core information.
Creel, P. (Halliburton Energy Services, Inc.) | Vavrek, G. (Halliburton Energy Services, Inc.) | Honnert, M. (Occidental Permian, Ltd.) | Kelley, R. (Halliburton Energy Services, Inc.) | Tate, R. (Halliburton Energy Services, Inc.) | Dalrymple, E.D. (Halliburton Energy Services, Inc.)
A team was formed to solve field-wide conformance problems in a San Andres (dolomite) unit of the Slaughter Field, Hockley Co., Texas. The team's goal was to increase oil production and decrease production costs by understanding fluid movement through the reservoir. The team was comprised of operating and service company personnel who performed data analysis, and developed engineering and solution designs.
The reservoir conformance team identified, quantified, and fully described field conformance problems. Team members focused on understanding fluid movement through the total reservoir rather than on single wells. They developed a framework of data for designing successful conformance solutions. This framework includes data about the reservoir, completion design, drilling and workover history, production and well-test history, logs, diagnostic analysis, and placement options. The framework serves as a data-collection tool and as a tool for identifying missing data.1
This project involves the following tasks: (1) analyzing approximately 300 wells, (2) identifying conformance candidate (pilot) wells, (3) implementing the pilot wells, (4) analyzing the results from the pilot wells, and (5) applying those results to the rest of the unit.
In this project, team members developed the typical data analysis, reservoir and production engineering proposals, and solutions proposals, and also thoroughly reviewed the customer's economic drivers. In these mature units, enhancing recovery and production rates and reducing costs were important factors.
Treatments have been placed and analyzed for sweep improvement, CO2 reduction, and water cycling-breakthroughs.
In early 1998, Occidental Permian, Ltd. (OPL) realized that, to make their large CO2 floods and waterfloods more financially viable, they needed to address conformance issues. OPL joined Halliburton Energy Services, Inc. (HES) to form a team for addressing conformance issues and for developing the step-function change.
The Slaughter Field in Hockley Co., Texas (approximately 40 miles west of Lubbock) is comprised of several units producing from the San Andres dolomite. Typical completion depths are 4,000 to 5,500 ft. This layered, highly dolomitized reservoir has significant permeability variations. The typical unit in this field has been on water flood for over 30 years, and many units have been on CO2 WAG for over 15 years. One of these WAG units was chosen as the focus of this project.
The Central Mallet Unit (CMU) operated by OPL is located in the Slaughter Field. Production is from the Permian-aged San Andres dolomite.
The CMU produces from an average reservoir depth of ±5,100 ft. The field was discovered in 1937 and unitized in February 1964, with full-scale water injection beginning shortly thereafter. CO2 injection began in December 1984. The wells in the CMU are completed with approximately 150 ft of 4 3/4-in. open hole, which increased the difficulty of choosing an appropriate placement technique.
The CMU was developed with "chicken wire" patterns, or a diagonal line drive in which producers and injectors line up along the WNW to ESE fracture trend. This development has led to many injectors communicating directly to the offset producers, which became even more evident when CO2 operations began in late 1984. Because of poor conformance results, producers began controlling gas breakthrough by reducing the gas-injection rate and altering the gas-to-water ratio (GWR). If these changes failed to control the gas volume being produced, the offending injector was then put on continuous water injection. A current study proposes pattern realignment, which could be a longer-term solution to the conformance problem despite its expense.
Darcy's law can not describe fluid flow accurately when the flow rate is high. In most cases in the recovery process, fluid flow is governed by Darcy's law. But when the flow rate is very high, for an instance, near the wellbore, Darcy's law is inadequate to describe fluid flow.
In 1901, Forchheimer put forward a classical equation, known as the Forchheimer equation, to make up the deficiency encountered by Darcy's law at high flow rates. He added a non-Darcy term into the Darcy flow equation. The non-Darcy term is the multiplication of the non-Darcy coefficient, fluid density, and the second power of velocity. One of the most important aspects in determining the non-Darcy effect is to estimate the non-Darcy coefficient as accurately as possible.
In this paper, theoretical and empirical correlations of the non-Darcy coefficient in one-phase and multi-phase cases in the literature are reviewed. Most researchers have agreed that the non-Darcy effect is not due to turbulence but to inertial effect. The non-Darcy coefficient in wells is usually determined by analysis of multi-rate pressure test results, but such data are not available in many cases. So, people have to use correlations obtained from the literature. This paper summarizes many correlations in the literature, and will provide a good reference for those who are interested in the investigation of the non-Darcy effect in the recovery process.
In most cases (not near the well-bore) in recovery processes, the flow pattern is governed by Darcy's law, which describes a linear relationship between pressure gradient and velocity as follows,
where u is superficial velocity, K is permeability, p is pressure, µ is viscosity, and x is dimension in x direction.
Forchheimer1 found that the pressure gradient required to maintain a certain flow rate through porous media was higher than that predicted by Darcy's law. He added a non-Darcy term to Darcy's law to account for this discrepancy, and the flow equation became
where ? is fluid density, and ß is called the non-Darcy coefficient in this paper. From equation (2), we see that the non-Darcy term is a multiplication of the second power of velocity, fluid density, and ß. There have been many names for ß. ß was called: the turbulence factor by Cornell and Katz,2 and Tek et al.;3 the coefficient of inertial resistance by Geertsma,4 and Al-Rumhy et al.;5 the velocity coefficient by Firoozabadi;6 the non-Darcy flow coefficient by Civan and Evans,7 Liu et al.,8 Grigg and Hwang,9 Narayanaswamy et al.,10 and Li et al.;11 the Forchheimer coefficient by Ruth and Ma;12 Inertial Coefficient by Ma and Ruth;13 the beta factor by Milton-Taylor;14 the non-Darcy coefficient by Thauvin and Mohanty,15 Cooper et al.,16 and Li et al.11 Equation (2) is called the Forchheimer equation by Ruth and Ma,12 Milton-Taylor,14 Ma and Ruth,13 Civan and Evans,17 Thauvin and Mohanty,15 Coles and Hartman,18 Cooper et al.,16 and Li et al.11
When flow rate is very high, Darcy's law is not adequate to describe flow pattern. High-velocity gas flow occurs in the near-well-bore region and condensate reservoirs. Non-Darcy effect is important in these regions according to Kalaydljian et al.19
Water injectivity decline is a very common phenomenon in waterflooding fields. Most of the previous analyses were focusing on water injectivity decline due to the migration of suspended particles in injection water or the injection water/reservoir fluid incompatibility. However, in some unconsolidated formations, another possible mechanism for water injectivity decline is sand mobilization, which means sand particulates separate from rock matrix and move into deep formation. This kind of injectivity decline is controlled by the operation condition of water injection such as injection pressure and injection rate. In this paper, a mathematical model is proposed to simulate the process of sand mobilization and the resultant water injectivity decline.
The mathematical model is derived based on material balance for water, sand particulate, and rock matrix. Also included in this model are particulate generation and deposition constitutive laws, and permeability-porosity correlation. Finite difference scheme is introduced to discretize the partial differential equations and the finite difference equations are solved implicitly through iteration. Sensitivity analysis is performed to study the effects of various factors on water injectivity decline and strategies for managing efficient water injection are proposed through the analysis.
Numerous researches[1-13] have been done in oil/gas well sand production. Because water injectors are not generally back produced and as a result very few researches were done in the past on injector sanding. Literature survey indicates that only a couple of papers[14-15] were published regarding this topic. Despite that, it does not mean sanding in water injectors is not a problem, instead, it can cause the injectivity decreases dramatically. As stated in reference, the injectivity of a well operated by Statoil in the Norwegian Sea decreased from 8000 m3/d to 0 m3/d in just half an hour which is tied to formation failure caused by the pressure waves generated during the sudden shut down of the pumps. From this single example, we can see that how bad it can be in water injectors once sanding occurs. Because of this, researches on sanding in water injectors are of the same importance as those in producers.
Santarelli et al presented a field case study on a reservoir operated by Statoil in the Norweigian Sea concerning the injectivity decline of water injectors. In this paper, it is believed that sanding is caused by the following reasons: 1) During well shut-in, the rock around the well is too weak to sustain the stresses and fails. 2) Because of the reservoir permeability heterogeneity, the wells are cross-flowing during shut-in and cause sand production in front of the perforated intervals. 3) The produced sand is not able settle down in the rat-hole before injection restarts and hence plugs the perforation tunnel. 4) As a result of the water hammer effect caused by well shut-in, the formation already weakened by sand production undergoes liquefaction that triggers large amounts of sand to be released in the well and hence killed the injectivity. Morita et al provided guidelines for completing water injection wells.
Many oilfield service companies as well as oil producers have manually tracked assets over the years for a variety of reasons. The service companies have tracked assets such as pumps, packers, or other products to assist in R&D efforts. Being able to collect the data and compile statistics on run times and component failures enables the service companies to evolve and improve current products as well as design new products. From a producers perspective, compiling the same or similar data allows for the development of "best practices" in operating procedures and processes. Software products developed to address these needs have evolved with time to provide more functionality. However, many systems implemented to handle the total process of data acquisition, warehousing, querying, and reporting to achieve improved operating results have become more difficult and expensive to support than the value added.
The value of the information has not diminished, but increased due to the fluctuations in oil prices and the continuing efforts to reduce lifting costs through design and process enhancements. The recent development of a web based tracking system incorporates workover management, downhole equipment, and chemical usage while enabling the operator and service provider the ability to easily enter and access the data. The system reduces the problems of database synchronization, multiple entries of the same data, and provides a common means through the Internet to interface with the information. The system links the operator in the field, the service company providing equipment or chemicals, and the district office together through a common database that each has access to.
The system allows a technician in a pump shop, workover foreman in the field, or chemical sales person to easily enter data into the system using a laptop computer or touch screen technology. The data is brought back to the service provider's local office and is accessible to the operator through the Internet. Wells, well equipment, and equipment components can be tracked for run life and root cause of failure. The operation becomes an information network that uses the same data to accomplish different tasks but with a common objective of reduced costs and improved profitability.
Ras Budran filed is a massive Nubian sandstone reservoir compartmentalized by major faults acting as partial barriers. Vertical communication is impeded by hydraulically sealing shale layers in the development of three main pressure regimes. A combined water injection and aquifer support the pressure. Pre-mature water breakthrough has been occurred in the middle of oil leg, which in turn limit the corrective action of the isolation of the watered out zones.
3D- geological and reservoir simulation model was constructed, based upon new reservoir characterization with the objective of improving the vertical definitions within the reservoir. The reservoir properties generated by deterministic interpretation for the new micro-zones.
This paper presents the approach taken to match 17 years of production data in particular, via aquifer definition and fault communication.
The history-matched model was then used to confidently check the production and injection well pattern and performance. The matched model then used to investigate the viability of infill wells to improve drainage pattern and sweep efficiency meanwhile increasing ultimate recovery.
Ras Budran field (R/B) is located at the eastern coast of the Gulf of Suez area (Fig 1). The field was discovered in April 1978 and production started in Feb., 1983. Production is maintained by gas lifting while the pressure is supported by a combined water flooding and limited aquifer drive.
There are 17 producing wells and 4 injectors over 3 offshore platforms. The field is relatively deep reservoir with the original oil water contact at 12350 ft-tvdss. Heavily under saturated reservoir with initial reservoir pressure of 5632 psia and the bubble point pressure 1200 psia. The structure contour map Fig. (2) shows the reservoir complexity.
The reservoir is massive sandstone and the macro layers were defined from top to bottom as follows; Raha, Nubian III, IIB, IIA and Unit I. Unit IIA has a shale/sand streaks that work as a vertical hydraulic barrier between the upper and lower units. Lower Units of block, which called Unit I (LA), Upper units of block A (UA), Upper Units of block B (UB) and Upper Units of block C (UC).
Fluid flow in the reservoir is directed from unit I to the juxtaposed upper reservoir units supported by water injection in unit I from injector A3b and direct aquifer support. Fluid flow in upper units of block A is only supported by the water injection through injectors A2, A1 and A9 respectively. Fig (3) illustrates the main cross section for the field.
Due to the nature of Ras Budran reservoir and its dipping structure, peripheral injection pattern was proposed and implemented in the original field development plans in October 1985 through two wells A2a (Unit IIB+III+R), and A3b (Unit I). In January 1990, the system was upgraded with another injector A1 (Units IIB+III), to replace the poor injector A2a and finally, the last injector A9a (Units IIB+III+R) was brought on line in April 1992.
The difference between formation and injection seawater salinity was used as a tool to monitor the flood front and the interblock communication. The field has already produced 80% from the estimated reserves.
Because it will not be possible to capture the dynamics of a water flood project, in particular with high mobility ratios as in the case of Ras Budran, with single cell calculations. Reservoir simulation modeling is essential in order to optimize injection/production well patterns and to optimize sweep efficiency via the injection distribution.
Numerous waterflooding projects are under way throughout the world for increased recovery. Water injection tests of oil zones are frequently undertaken during the planning phase of waterfloods. Analysis of the bottomhole pressure data recorded during these tests not only provides similar information to that obtained from production tests concerning the well and the reservoir characteristics but also allows the mobility ratio between the injected and resident fluids to be determined.
Conventionally, pressure fall-off test data is analyzed using semilog plot of bottomhole pressure versus time. This paper is the extension of the Tiab's Direct Synthesis Technique10-15 to pressure injection and Fall-off tests in water injection wells.
Direct synthesis is a transient pressure analysis technique10-15, which uses log-log plot of pressure and pressure derivative vs. time. Thus, different straight line portions indicating different flow regions are directly analyzed. Direct synthesis is very useful in conditions of short and early time pressure data missing tests. It also verifies the results since it uses more than one equation for the estimation of reservoir parameters such as permeability, wellbore storage coefficient, and skin factor.
Finally, field examples of pressure falloff analysis are presented to illustrate use the direct synthesis and results are compared with those from type curves and conventional semilog analysis.
Traditionally water flood schemes have been implemented later in the life of the field following primary depletion. Now, such schemes are often considered during the initial development of a field. The economic viability of many fields depends upon successful implementation of water injection at early stage. Injection tests are, therefore, performed on appraisal wells drilled prior to the decision to develop the field. These tests are designed to assess both the efficiency of the filtration equipment and the injection characteristics of the formation. Operational and the cost considerations dictate that the maximum possible information be derived from these tests, which may be few hours of duration.
Analysis of the pressure Falloff and injectivity tests has been discussed at considerable length in the literature. The pressure buildup during injection period, however, has received relatively little attention. The main reason is that falloff tests match to the pressure buildup test in production wells, which is easy to analyze. Furthermore, the injectivity test is mathematically difficult to handle due to moving boundary, the flood front.