Question

What is the difference between characterization testing (also known as validation) and production testing?

Answer 1

Production testing;

Production tests are run to obtain an indication of well productivity. Some production tests are performed in open hole (such as drill stem tests) and can be used in making completion decisions. Others (such as single-point, multipoint, and swab tests) are performed after the well is completed and generally involve routine measurements of oil, gas, and/or water production under normal producing conditions. The test results can be used to determine reservoir properties, to assess the degree of damage or stimulation, to identify production and reservoir problems, or to satisfy the reporting requirements of regulatory bodies. Production tests can also be performed when more conventional well tests (such as pressure drawdown and buildup tests) are impractical due to time constraints, well conditions, or extremely low well productivity.

Drill stem tests;

Drill stem tests (DSTs) are used to obtain (1) samples of the reservoir fluid, (2) measurements of static bottomhole pressure, (3) an indication of well productivity, and (4) short-term flow and pressure buildup tests from which permeability and the extent of damage or stimulation can be estimated.^[2] A DST is run in the open hole after drilling, and is often used in deciding whether to complete a particular zone. The total test duration is frequently a function of hole condition, and the tool assembly must be retrieved from the open hole after the test is completed (see Drill stem testing).

To run a drill stem test, a special DST tool is attached to the drill pipe and run in the hole opposite the zone to be tested. A DST tool typically includes two or more clock-driven, bourdon-tube recording pressure gauges, a set of flow valves, and one or two packers. The tool isolates the formation from the mud column in the annulus. When the tool is opened, reservoir fluid can flow into the drill pipe (and possibly to the surface); pressure is recorded continuously during the test.

Most DSTs (Figure 1) consist of two flow periods and two shut-in periods. The pressure gauges record the initial hydrostatic mud pressure (p_ihm) while going into the hole. The initial flow period (p_ifl to p_ffl) is a short production period, usually only 5 to 10 min. Pressure rises during the flow period as fluid collects in the drill stem above the pressure gauges. The objective is to release the hydrostatic mud pressure and draw down the formation pressure only slightly. The first shut-in period (p_ffl to p_isi) should be long enough to allow the reservoir pressure to return to its initial value. A shut-in time of 1 hour is usually preferred.

In the second flow period (p_if2 to p_ff2), the objective is to capture a large sample of formation fluid and to reduce the pressure as far into the reservoir as possible. This flow period should be at least 1 hour, and if reservoir fluid is produced to the surface, flow rates should be measured. The second shut-in period (p_ff2 to p_fsi) is longer than the first and is used to estimate formation properties in a manner similar to that for analyzing conventional buildup tests. The duration of the final shut-in period depends on well behavior during the flow test; it may range from one-half to twice the flow time. Comparison of the final or extrapolated reservoir pressure from this second shut-in period to that from the initial shut-in period may suggest depletion has occurred during the DST. If so, the zone being tested is a limited, noncommercial reservoir. Following the second shut-in period, the final hydrostatic mud pressure is measured (p_fhm) and the DST tool is pulled from the hole.

Single-point tests

Single-point tests are usually simple productivity tests that typically involve a measurement (or estimate) of initial or average reservoir pressure and a measurement of flow rate and flowing bottomhole pressure (which can be estimated from flowing surface pressure) at stabilized producing conditions.^[3] From these data, the productivity index, PI, can be calculated as follows:

• 

where

• q = flow rate (STB/day or MSCF/day)
• = initial or current average reservoir pressure (psia)
• p_wf = flowing bottomhole pressure (psia)
• ? = viscosity (cp)
• B = formation volume factor (rcf/MSCF)

The productivity index can be a useful indicator of well productivity and wellbore condition during the life of a well. PI will generally decrease over time due to declining reservoir pressure, changes in producing conditions, and/or production problems.

Single-point tests can also be used to estimate formation permeability^[4] with an iterative solution of the transient radius of drainage equation (Equation 2) and the pseudosteady-state flow equation (Equation 3), as follows:

• 
• 

where

• r_d = transient radius of drainage (ft)
• k = permeability (md)
• t = flow time (hr), generally best estimated by dividing cumulative fluid production by flow rate, q
• ? = porosity (fraction)
• c_t = total compressibility (psia^–1)
• B = formation volume factor (rb/STB for oil or rb/MSCF for gas)
• r_w = wellbore radius (ft)
• h = net pay (ft)
• s? = apparent skin factor

To solve for permeability, an arbitrary value of permeability is assumed (0.1 md is often a good first estimate), and Equation 2 is solved for r_d. Then, this value for r_d is used in Equation 3 to solve for permeability. For each iteration after the first, use the permeability calculated from Equation 3 in solving for r_d from Equation 2; this procedure usually converges in three to four iterations.

The need to estimate an apparent skin factor, which is usually not known, is the biggest limitation of this method. Pressure buildup tests run in other wells in the same reservoir often provide a good estimate of typical skin factors. Low permeability wells are generally broken down and balled out after completion and prior to testing; in these wells, a skin factor of –1 to –2 is often a reasonable assumption. If a well has been damaged by drilling fluids and the perforations have not been broken down, a skin factor of +2 to +5 (or more) is appropriate (see Fluid flow fundamentals).

The single-point test method for estimating permeability is valid for constant flow rate production, constant bottomhole pressure production, or smoothly changing bottomhole pressures and flow rates. The method is recommended for estimating permeability from prefracture flow test data only; it does not work well with postfracture flow data. The method is particularly useful in low permeability reservoirs where operators do not run buildup tests routinely because of the long test times required to overcome wellbore storage effects and reach radial flow (see Pressure transient testing).

Characterization test;

In computer programming, a characterization test (also known as Golden Master Testing^[1]) is a means to describe (characterize) the actualbehavior of an existing piece of software, and therefore protect existing behavior of legacy code against unintended changes via automated testing. This term was coined by Michael Feathers.

Advantages;

Golden Master testing has the following advantages over the traditional assertions-based software testing:

• It is relatively easy to implement for complex legacy systems.
• As such allows refactoring.
• It is generally a sensible approach for complex results such as PDFs, XML, images, etc. where checking all relevant attributes with assertions would both insensible due to the amount of attributes and result in unreadable/unmaintainable test code.

Disadvantages;

Golden Master testing has the following disadvantages over traditional assertions-based software testing:

• It depends on repeatability. Volatile and non-deterministic values need to be masked / removed, both from the Golden Master as well as from the result of the process. If too many elements need to be removed, or removing them is too complex, it can render Golden Master testing impractical.
• It depends not only on the software to be repeatable, but also on the stability of the environment and input values.
• Golden Master testing does not infer correctness of the results. It merely helps detecting unwanted side effects of software changes.