Energy Data and PSG Applications Prove Instrumental in Creating a Regional Pressure History Database for the Gulf of Mexico Deep Shelf

Industry Challenges
Engineers use pressure data over time to determine well connectivity and make detailed assessments of the remaining producing potential of a reservoir. However, determining reservoir pressure over time has often required that engineers shut down wells and halt production to perform difficult pressure tests. Taking such measurements on a regular basis is very costly and disruptive to production. Even then, companies can only determine pressures in their own wells, as opposed to gathering a more comprehensive picture of an area. Yet, pressure data is critical for allowing operators to perform best practices, such as material balance analysis, that will allow them to better understand rates, reserves and drive mechanisms.

As part of the study, mud weight data from 6,000 wildcat wells was used to define areas where pressure is abnormally shallow relative to regional trend. The resulting residuals map helps identify reservoir compartments and lateral seals across the Shelf.

The IHS Advantage
Pressure data played a key role in the unprecedented Gulf of Mexico (GOM) Deep Shelf Production Performance Study, conducted by PetroSolutions, a Houston-based E&P consulting firm, in conjunction with IHS. IHS initiated the study, which involved a unique and in-depth analysis of 94 existing GOM fields (64 active) in water depths less than 1,000 feet, to help E&P companies make more informed decisions about production optimization or market entry into this new, technically challenging, but potentially lucrative operating environment.

In the course of completing the extensive study, Tom Harris, founding partner of PetroSolutions, and his team employed a multi-step process to accurately determine pressure over time, without requiring disruptive or costly measures. Harris derived pressures over time using IHS's Gulf of Mexico Formation Pressure Database, other IHS U.S. databases and several applications from the Producing Systems Group (PSG). Harris' approach holds promise for those looking to take advantage of the insight pressure data offers, but to do so in an efficient and cost effective manner.

"The industry has traditionally required additional reservoir pressure data to do material balance," Harris explained. "They always felt like they had to stop production, do a pressure test and turn the well back on. We said, 'Let's use the production tests to make an estimate of the reservoir pressure through time and we are not shutting in any wells while doing this.'"

Arriving at Shut-in Bottomhole Pressures
Since no comprehensive regional database of shut-in bottomhole pressures exists, PetroSolutions approached the challenge of creating a database of calculated pressures with two principles: Verify every calculation with all available data, and record each step in the sequences of calculations in the study's Microsoft Access® database. Recording interim calculations ensures that the team's methodology is not a "black box," and that engineers who want to apply different methodologies can pick up the calculations at any point in the sequence.

Louisiana Deep Shelf Pressure vs. Depth Plot

With the Gulf of Mexico Formation Pressure Database, explorationists and engineers can plot pressure vs. depth to help predict overpressured zones and thereby enhance safety and production goals during exploration drilling. Above, a selected portion of more than 1,000 wells in offshore Louisiana reflect the transition from normal pressure through to the overpressured zone.

Harris began his analysis with the most dense well coverage data available: IHS's production data in the 298 format. Using PERFORM software, he applied gradient analysis to predict initial flowing bottomhole pressure. As a verification step, he again used PERFORM to study inflow performance relationships, comparing pressure values to ensure they matched up.

From the resulting data, Harris could infer permeability thickness and, using those estimates, once again turn to PERFORM to estimate flowing bottomhole pressures over time by modeling a shutdown of flow in each well. The result: a complete database of bottomhole pressures over time.

The next step was to verify these estimates using available measured pressure data, which is less dense in coverage but spread over all the reservoirs in question, since each had at least one wildcat well. For this data, Harris and his team turned to IHS's scout ticket data in the 297 format and its unique Gulf of Mexico Formation Pressure Database. Mud weight data from 6,000 wildcats allowed him to estimate pore pressure across formations. The resulting map of residuals showed compartmentalization and lateral seals. Then with net sand data from a little-known database within PI/Dwights PLUS - the former TENROC database, with correlated tops from 16,000 GOM wells - he created a map showing sand pileup. Ultimately he looked for mud weight pressure to be slightly over calculated shut-in pressure (overbalanced for drilling safety considerations), and the results backed up previous calculations with a very high degree of confidence.

With the verification process complete, and a Shelf-wide coverage of reservoir pressure over time, Harris could then perform a variety of analyses. He could, for example, determine reservoir compartmentalization by grouping clusters of adjacent wells with similar pressure, flow and rate.

Harris feels strongly that, although this information is essential to fully understand reservoirs, few E&P companies take advantage of pressure data. In a highly compartmentalized play like the Deep Shelf, pressure data can give a tremendous boost to an asset team's level of confidence that reservoirs are defined correctly. Using production data from all operators, not just a single company's wells, gives teams a much broader sample rate, resulting in higher-resolution earth models.

"Pressure is a powerful tool that is woefully under-utilized," Harris explained. "The GOM basins, like most basins worldwide, are compartmentalized within fields. Basins are compartmentalized. And pressure is the most powerful tool to help identify those compartments. Compartments equate to continuity - and opportunities."

Determining Rock and Fluid Properties
Harris' next step was determining fluid properties with IHS's PVTLIB program, which employs one of the industry's most extensive libraries of empirically derived models, with more than 150 PVT correlations for oil, gas, condensate and water.

"PVTLIB allows me to understand the compositional and phase changes of the rude oils or the gases or condensates as I change the pressure for the range of temperatures," Harris said. "That is a tremendous benefit and I get information such as formation volume factors, bubble-point pressures, dew pressures, and critical temperatures and pressures. It's all in PVTLIB."

Material Balance Analysis with OilWat/GasWat
Having determined which wells belonged to which reservoirs, Harris and his team could perform material balance analysis to understand drive mechanisms, compressibility contributions, Expected Ultimate Recovery (EUR) and orginal-hydrocarbons-in-place.

Harris fed the fluid values derived from PVTLIB into OilWat/GasWat, also part of IHS's PSG software suite. OilWat/GasWat allowed him to perform material balance quickly, so he could get a reservoir-based analysis and gather a complete picture of reservoir performance an potential. Harris considers material balance a much more thorough means of assessment than standard decline curve analysis.

"Pressure is a powerful tool that is woefully under-utilized. The GOM basins, like most basins worldwide, are compartmentalized within fields. Basins are compartmentalized. And pressure is the most powerful tool to help identify those compartments. Compartments equate to continuity - and opportunities. "

— Tom Harris, PetroSolutions

Using sophisticated data analysis models, the software calculates original oil-in-place (OOIP) and original gas-in-place (OGIP) for reservoirs with or without water influx, as well as projecting future flow streams, and matches that data with historical trends. For the study, he first determined original hydrocarbons in place and the drive mechanisms.

Harris then performed forward simulation in OilWat/GasWat to predict flow streams of oil, gas and water, and the pressures in the future. Ultimately, this allowed him to develop an accurate prediction of future performance that was history-matched to past performance.

ENGINEERING ANALYSIS — Role of Pressure Data

IHS's production studies utilize a wide range of robust engineering analysis techniques. Many techniques rely on pressure history, which can be hard to find beyond an operator's own wells. The study team utilized IHS's extensive GOM well and production data to derive a database of Shelf-wide reservoir pressure data over time.

Industry Applications
The process Harris used holds benefits for engineers, geologists and geophysicists. With a database of reservoir pressures over time, each group can perform critical further analysis.

Engineers can more easily pinpoint their next wells by determining reservoir compartmentalization by grouping clusters of adjacent wells with similar pressures, flows and rates as a first pass "without even looking at a log or geo-model." Furthermore, they can analyze all available wells in an area, not just their own.

Additionally, they can perform material balance analysis to understand drive mechanisms after just 5 to 15 percent reservoir drainage, and determine OGIP and OOIP.

With mud weight data, geologists can estimate pressure and subtract observed data from regional dip. The resulting maps of residuals, in the Deep Shelf basins, shows compartmentalization and lateral seals. By adding in net sand data, they can see areas of sand depocenters and look for promising trends or pathways to adjacent areas of interest.

Researchers determined estimates for pore pressure by deriving density conversions using mud weight data, predicting pore pressure for shale from sonic logs and/or resistivity logs, and matching pore pressures with seismically generated seal detectors. The result was a pore pressure profile that can be matched with 3 separate seismic (AVO) signatures.

Geophysicists can derive a pore pressure profile that can be calibrated to their seismic data by using the mud weight data, production test derived data, the IHS Gulf of Mexico Formation Pressure Database, and pore pressure estimates derived from electric and/or sonic logs from IHS's Log Services group. Harris and his team found this where their analysis revealed a very specific seismic signature for each layer in the "buried bottle" pressure regime: inside or outside of the bottle, or within the wall of the bottle. Using this analysis, they were able to match 39 AVO signatures to one of three ultimate results: good well, dry hole, or marginal producer.

"The AVO findings alone entirely justify the time and expense of this pressure study," Harris says. "But ultimately, in terms of best practices for engineers, the availability of a benchmark database of pressure history has enormous value in exploration, exploitation, production and operations planning."

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