A New Method for Gas Well Deliverability Potential Estimation Using MiniDST and Single Well Modeling: Theory and Examples
- Karthik Kumar Natarajan (Schlumberger) | Sameer Joshi (Schlumberger) | Raj Banerjee (Schlumberger) | K.M. Sundaram (ONGC)
- Document ID
- Society of Petroleum Engineers
- SPE Indian Oil and Gas Technical Conference and Exhibition, 4-6 March, Mumbai, India
- Publication Date
- Document Type
- Conference Paper
- 2008. Society of Petroleum Engineers
- 5.5.11 Formation Testing (e.g., Wireline, LWD), 5.6.1 Open hole/cased hole log analysis, 4.3.4 Scale, 5.6.4 Drillstem/Well Testing, 5.6.8 Well Performance Monitoring, Inflow Performance, 1.6.9 Coring, Fishing, 5.3.2 Multiphase Flow, 1.8 Formation Damage, 1.2.3 Rock properties, 2.2.2 Perforating, 5.2 Reservoir Fluid Dynamics, 5.5.3 Scaling Methods, 1.10 Drilling Equipment, 4.1.5 Processing Equipment, 4.1.2 Separation and Treating, 5.1 Reservoir Characterisation, 3.3.1 Production Logging, 4.2 Pipelines, Flowlines and Risers, 2.7.1 Completion Fluids, 4.6 Natural Gas, 5.1.5 Geologic Modeling, 1.14 Casing and Cementing
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This paper presents techniques for interpretation of Mini-Drill Stem Test (MiniDST) for establishing commingled Absolute Openhole Flow Potential (AOFP) in deep water exploration wells in India. These gas bearing reservoirs are vertically heterogeneous with high permeability.
MiniDST's are conducted using the inflatable straddle packer system of wireline formation tester. A MiniDST transient sequence consists of a single or multiple flow periods, induced using a downhole pump, followed by a pressure buildup. The objectives of a MiniDST are sampling, estimation of reservoir properties such as permeability (k), skin(s), radial extrapolated pressure (p*) and estimating AOFP. AOFP is an important gas well flow parameter and is used to determine the commerciality of discovered prospects. We use a two step approach in establishing commingled AOFP of gas wells. First, we conduct a multiple station MiniDST run and interpret the data to estimate reservoir parameters (k, s, and p*). We also compute non-Darcy flow coefficient (D) using Swift & Kiel expression and then use an analytical pseudo-steady state equation to establish single point AOFP for each of the tested zones. Second, we extend routine forward modeling and incorporate features such as scaled permeability data, rock types and hydraulic flow units through interpretation of Nuclear Magnetic Resonance (NMR) and wireline petrophysics, into a model. The model is built in two different ways. One is based on numerical simulator and another based on cumulative permeability-thickness product for the gas bearing zones, using average reservoir pressure and temperature for the whole zone of interest. The success of single well simulation has given us the capability to forecast total AOFP for multiple zones using commingled approach. Furthermore, we also included production tubular and choke in our simulation model for well deliverability estimation.
Our technique has resulted in immense saving in rig time and cost since the workflow allowed delivering answers which enabled us to determine AOFP without resorting to conventional four points deliverability testing.
Deliverability testing of gas wells is based on theory of transient and pseudosteady flow of gases (Lee, 1982). Traditionally, different testing procedures like flow-after-flow, isochronal and modified isochronal are used to estimate parameters required to provide deliverability estimates.
The turbulent or non-Darcy flow effects close to the wellbore, which appear as rate-dependent or non-Darcy skin, requires gas wells to be tested at a number of rates with the above mentioned tests so as to be able to estimate the non-Darcy flow coefficient by separating the mechanical skin component from the total skin factor (st ). All these multirate methods of interpretation require well tests of quite long durations (Horne and Kuchuk, 1988).
Kabir (2006) suggested a two step approach based on multirate transient drawdown tests, followed or preceded by a buildup. Firstly, he estimates reservoir parameters (k, s, D and p*) with transient data, rather than doing the traditional deliverability calculation with four points. Then he uses these parameters to predict future deliverability by forward simulations with an analytic tool.
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