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Working with pore pressure

Tuesday, July 13, 2010 in Feature Articles

Geotrace Technologies of Houston is developing better ways to estimate pore pressure data from seismic. It is very useful to know what kind of pressures you are likely to encounter when you drill. Chief technology officer Gary Yu explains

Understanding formation pore pressure distribution is critical for evaluating seal integrity and hydrocarbon accumulation column height evaluation of a prospect.

You can also work out pressure attributes such as fracture gradient (amount of pressure you need to induce fractures in the rock at different depths), correct mud weight required in drilling, effective stress, and overburden pressure.

With this information, drillers and engineers can better carry out well planning for mud program, casing shoe position, and casing string purchase as well as assess drilling risk for wellbore stability, hazard prevention, and reservoir formation protection.

Pore pressure also offers tremendous value in the early phase of exploration.

It allows us to assess trap seal integrity and hydrocarbon accumulation column height for prospect evaluation, as well as to identify bypass zones and pressure changes when 4D data are available.

It has been shown that, using seal integrity concept, you can differentiate low saturated gas sands, i.e., a seal breach problem, from saturated sands.

This seal breaching analysis using pressure data can complement the AVO analysis in differentiating false anomalies caused by low saturation from valid bright spots.

Pennebaker showed in 1970 that pore pressure can be predicted from seismic velocities, and since then many formulations have been introduced with varying success.

Prestack seismic data quality in terms of signal-to-noise ratio (SNR) and resolution can significantly affect velocity analysis and the quality of pore pressure estimation.

Steps

Geotrace developed a systematic methodology together with a calibrated pressure model that transforms seismic interval velocities into high density high resolution (HDHR) formation pore pressure and subsequently other pressure related attributes.

This calibrated pressure model attempted to go beyond conventional compaction trend consideration and take into account the effect of burial depth, porosity, temperature, and shale diagenesis.

First, PSTM (pre-stack time migrated) seismic data was carefully optimized to enhance its signal to noise ratio and frequency bandwidth.

Then, HDHR anisotropic velocity analysis with 6th-order curved ray formulation was employed to accurately extract a detailed 3D seismic velocity field for every time sample at every common-mid-point (CMP) location.

Land and marine seismic data were used to evaluate the progressive impact of data quality on the accuracy and vertical resolution of the seismic interval velocity field at important milestone steps in the prestack data enhancement workflow.

A calibrated pressure model was built after it was correlated with available wells, logs, and drilling data and tested to evaluate its reliability and uncertainty of pore pressure estimation.

With blind well testing and real case studies, it was demonstrated that this methodology is practical and effective and can provide valuable information for prospect evaluation, well planning, and drilling risk management ahead of the drill bit.

Calibrated pressure model

A calibrated pressure model was developed to transform seismic interval velocities to pore pressure.

It takes into consideration various factors including under compaction of the rock, burial depth, temperature, shale diagenesis and inelasticity, that affect subsurface pore pressure.

Available logs, drilling data, and engineering information such as mud weights, LOT (leak-off test), RFT (retrievable formation tester), and MDT (Modular Formation Dynamics Tester) from nearby wells were incorporated into the calibrated pressure mode generation.

Figure 2 displays the estimated mud weights using a calibrated pressure model at a known well location and at a blind well test location from a project in the deepwater Gulf of Mexico.

The distance between the two wells is in tens of miles, and the target is around 16,000 feet. The real mud weights applied are denoted as blue triangles and the estimated mud weights are shown as solid red trend.

The mud weights vs. depth chart at the calibration well (to the left of Figure 2) illustrates that the calibrated pressure model predicts quite well the pore pressure in terms of mud weights and thus demonstrates its applicability and reliability.

When this model was applied to the blind well test using derived high resolution interval velocity field, the estimated mud weights were very close to the drilling mud weights and RFT (retrievable formation tester) measurements as shown in the chart to the right in Figure 2.

In addition, the calibrated pressure model also provides an uncertainty assessment displayed as a fairway between the green and light blue solid trends around the estimated mud weigh trend.

The relative tight and consistent fairway displayed in Figure 2 implies a good pressure model was generated and the estimated pressure result was reliable.

Figure 2: The left chart is the estimated mud weights at the known well and the right chart is the estimated mud weights at the blind well test location. The estimated mud weights are very close to the known mud weights and RFT values.

Case study

The following study was conducted using the above workflow and calibrated pressure model to estimate the reservoir and surrounding formation pore pressure and appraise the prospect seal integrity for an onshore pressure-charged gas play in the Gulf Coast region of the United States.

In Figure 3, a 2D pre stack time migrated section (seismic in black wiggle with variable area) overlaid with estimated pore pressure in color (blue to orange for high to low pressure variation) demonstrates that the prospect has excellent fault and top seals because there are no pressure leaks on either sides of the two bounding faults (in white) and no leakage into the overlaying formation.

The drill bit found gas and confirmed the pore pressure estimation and seal capacity interpretation.

Figure 3: Estimated pore pressure (blue to orange for high to low pressure) overlaid with PSTM stack (black wiggle with variable area). The prospect is well-sealed by faults and top formation.

A detailed two dimensional display in Figure 3 can assist the evaluation of subtle pressure changes across lithological, stratigraphic, and structural boundaries, but does not give a good idea of spatial pressure variation and 3D pressure cell distribution.

On the other hand, a 3D visualization offers a better view of regional pressure distribution.

Figure 4 (below) is a snapshot of a 3D visualization exercise of pore pressure distribution for the same prospect in Figure 3.

Now, explorationists can quickly visualize and interpret 3D pressure cell distribution, pressure plume, and pressure sink as well as quickly assess any potential seal breaching problems and drilling problems in a region or a basin.

Figure 4: Estimated pore pressure in 3D visualization shows 3D pressure cell distribution and regional pressure variation.

Another powerful 3D visualization is to simultaneously co-render multiple 3D attribute volumes for integrated interpretation.

Figure 5 (below) depicts a 3D visualization of a PSTM seismic cube embedded in various pressure attribute cubes, since PSTM stack volume shows structure and stratigraphy better, and pore pressure volumes show fluid dynamics better.

It allows explorationists from different disciplines to work together and perform true 3D interpretation by moving, stripping, or intercepting various sub-cubes to evaluate geology, structure, reservoir, pressure, and their interaction – a full integration allows conducting geological and geophysical evaluation by geologists and geophysicists and planning well design and drilling hazard prevention by drillers and engineers.

Figure 5: A series of snapshots of a 3D visualization and interpretation session using a combination of multiple 3D PSTM and HDHR 3D pressure attribute volumes.

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