UNCONVENTIONAL WELL GAS TO OIL RATIO CHARACTERIZATION

Abstract

A method of reducing gas flaring through modelling of reservoir behavior using a method of optimizing oil production from one or more well(s) in a reservoir, the method providing a model of the well, inputting well data for a one or more well(s) into the model, the well data selected from geological layers, reservoir properties, fracturing data, completion data, permeability data, geochemistry, and combinations thereof. Inputting historical production data from one or more well(s) into the model, the historical data selected from PVT data, BHP, oil production rates, gas production rates and water production rates, or combinations thereof. Controlling the model to match one or more parameters selected from production rates, gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), or a combination thereof in a probabilistic manner to obtain a plurality of historical models. Verifying one or more test models against the historical models to identify an optimal model with minimum error. Using the optimal model to predict one or more parameters selected from production rates, gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), or a combination thereof from the well into a future. Optimizing a production plan using the predicted parameters and implementing the optimized production plan in said well, whereby oil production is optimized as compared to a similar well produced without the optimized production plan.

Description

PRIOR RELATED APPLICATIONS

This application claims priority to 63/196,648, filed Jun. 3, 2021, and incorporated by reference in its entirety for all purposes.

FEDERALLY SPONSORED RESEARCH STATEMENT

Not applicable.

FIELD OF THE DISCLOSURE

The disclosed methods relate generally to the optimization of a reservoir production, in particular gas to oil levels.

BACKGROUND OF THE DISCLOSURE

With technically recoverable reserves estimated by United States Geological Survey (USGS) to be between 4.4 and 11.4 billion barrels, Bakken is one of the earliest hybrid unconventional plays to be developed into a mature asset by the industry. The Williston Basin contains a complete stratigraphic record from the Cambrian to Tertiary with sediment thickness of over 16,000 feet with multiple conventional and unconventional targets that have been exploited over the last 40 years.

The Bakken Formation within the Williston Basin has three main reservoir targets and two potential source rocks (FIG. 1A-B): the Upper Bakken Shale and the Lower Bakken Shale source rocks were deposited in a sub-oxic to anoxic offshore marine depositional environment with a stratified water column, whereas the Middle Bakken (MB) member was deposited in a marine to marginal setting under oxic conditions. The units of the Three Fork members like the Upper Three Forks (UTF) and the Middle Three Forks (MTF) are mainly cyclical deposits of wind-blown silts deposited in shallow wet lacustrine environment that are interbedded with green silty dolomitic claystones.

The Williston basin and the Bakken formation horizontal development started in the early 2000's and ramped up into the latter part of the decade. Many of these wells were drilled in single well drilling space units (DSUs) with smaller intensity, sleeve completion designs. The most common artificial lift (AL) used early on was rod pump. As field oil production ramped up in 2015, the associated gas also peaked, but the gas to oil ratio (GOR) was relatively stable or slowly increasing. See FIG. 2.

As more development and investment was made in 2017/2018, we also saw more intense completion designs, tighter well spacing, AL optimization, and depletion effects with it. As the oil peaked again in 2019, this time the fieldwide GOR was much higher and with that came record high gas production. Many operators and gas gatherers capacity were overwhelmed, and higher flaring was seen throughout the field. This in conjunction with the higher attention on the environment created a greater focus and collaboration in Bakken to handle the gas. As more offtake capacity was created, operators also came forward with innovative solutions and uses for the excess gas.

We continue to see ever increasing GOR and gas rates throughout the field, highlighting the importance of accurate forecasting and prediction of GOR. The proper forecasting will allow us to maximize production, increase economics, and do so in an environmentally friendly manner. This invention addresses one or more of these needs.

SUMMARY OF THE DISCLOSURE

With the increased focus on the environment, the oil and gas industry is taking aggressive action to reduce greenhouse gas (GHG) and flare emissions. As an important part, characterizing the GOR from Unconventional Resources (UR) wells will help predict long-term gas production trends and develop high GOR mitigation strategies. In this disclosure, the short- and intermediate-term GOR behaviors are discussed based on Bakken wells with different completion designs and the key drivers and mechanisms dictating the GOR trends are investigated. A novel modeling workflow is developed to match GOR & other observed data and forecast long-term GOR trends. Potential techniques to mitigate rising GOR are also proposed.

The GOR data from a large number of wells were analyzed to identify GOR correlations with fluid properties, completion design, depletion, drawdown strategy and artificial lift. Multiple types of data were collected in this study in conjunction with production data, including long-term bottom hole pressure (BHP), pressure volume, temperature data (PVT), interference test and shut-in data. Data driven reservoir models were built to match the GOR behaviors for both old sliding sleeve completion wells and modern plug-and-perf completion wells. Long-term GOR trends were predicted with the calibrated reservoir model.

The key insights and conclusions from this work included: 1) Bakken unconventional GOR shows conventional black oil reservoir GOR behaviors, but it also has many unconventional characteristics due to the low matrix permeability and different types of hydraulic fracture systems created. 2) GOR trends are sensitive to pressure drawdown. Strong correlations between GOR and BHP are observed. 3) Old sliding sleeve completion well GOR shows cyclical rise-and-fall patterns with a gradually increasing long-term trend, whereas modern completion wells show rising GOR trend after their BHPs drop below bubble point pressure (P_b) and then reach an intermediate-term plateau. 4) Offset parent well depletion drives child well GOR to rise faster and to a higher level. 5) Less aggressive drawdown, recharging due to frac hits and long shut-in can delay/mitigate GOR rising. Finally, 6) Bakken well GOR behaviors can be accurately modeled using the proposed approach.

The learnings presented herein improve our understanding of unconventional well GOR trends in the industry. Better GOR characterization will improve forecasting for offtake capacity and flare emission reduction and help the industry to meet environmental standards. The analysis and modeling approaches proposed herein can also spark further research and development activities.

Although GOR is specifically exemplified herein, the method is not so limited and can be used to optimize other well parameters, as desired by the operator.

The present methods include any of the following embodiments in any combination(s) of one or more thereof:

A method of optimizing oil production from a well in a reservoir, said method comprising:
a) providing a model of said well;
b) inputting well data for a plurality of wells into said model, said well data selected from
   geological layers, reservoir properties, fracturing data, completion data, permeability data,
   geochemistry, and combinations thereof;
c) inputting historical production data from said plurality of wells into said model, said historical
   data selected from PVT data, BHP, oil production rates, gas production rates and water
   production rates, or combinations thereof;
d) controlling said model to match one or more parameters selected from production rates,
   gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), or a
   combination thereof in a probabilistic manner to obtain a plurality of historical models;
e) verifying one or more test models against said historical models to identify an optimal model
   with minimum error;
f) using said optimal model to predict one or more parameters selected from production rates,
   gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), or a
   combination thereof from said well into a future;
g) optimizing a production plan using said predicted parameters;
h) implementing said optimized production plan in said well, whereby oil production is
   optimized as compared to similar well produced without said optimized production plan.
A method of reducing GOR in oil production from one or more well(s) in a reservoir, said method
comprising:
a) providing a model of said one or more well(s);
b) inputting well data from one or more well(s) into said model, said well data selected from
   geological layers, reservoir properties, fracturing data, completion data, permeability data,
   geochemistry, and combinations thereof;
c) inputting historical production data from said one or more well(s) into said model, said
   historical data selected from PVT data, BHP, oil production rates, gas production rates and
   water production rates, and combinations thereof;
d) controlling said model to match one or more parameters selected from production rates,
   gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), or a
   combination thereof in a probabilistic manner to obtain a plurality of historical models;
e) verifying one or more test models against said historical models to identify an optimal model
   with minimum error;
f) using said optimal model to predict gas to oil ratio (GOR) from said one or more well(s) into
   a future;
g) optimizing a production plan using said predicted GOR to reduce gas production;
h) implementing said optimized production plan in said well, whereby GOR is reduced as
   compared to said predicted GOR.
Any method herein described, wherein said reservoir is an unconventional reservoir.
Any method herein described, wherein said reservoir is a hybrid shale and limestone reservoir.
Any method herein described, wherein said model in a) has finer gridding near said well.
Any method herein described, wherein said finer gridding is Tartan gridding.
Any method herein described, wherein said optimized reservoir plan includes reduced drawdown to
reduce GOR.
Any method herein described, wherein said optimized reservoir plan includes widening fracture
cluster spacing to reduce GOR.
Any method herein described, wherein said optimized reservoir plan includes recharging high GOR
parent wells with offset child well fracture hits to reduce GOR.
Any method herein described, wherein said optimized reservoir plan includes extending well shut-in
to reduce GOR.

As used herein, a regular grid is a network of crossing right angle lines, such as is seen on graph paper. In reservoir modelling, grids may be two dimensional and need not be at right angles or regular. A common requirement in reservoir simulation is an increased level of detail around an item of interest such as a well. A “tartan” grid has variable height and/or width of the lines and is a gridding style available in some reservoir modelling programs.

As used herein, a “reservoir” is a formation or a portion of a formation that includes sufficient permeability and porosity to hold and transmit fluids, such as hydrocarbons or water or natural gas, and the like.

A reservoir can have a plurality of chemically distinct “zones” therein, particularly in very tight rock, where mixing is almost non-existent. The data herein can be catalogued by zone, allowing that portion of the data to be used for other zones, even in other wells, as long as the zone has similar fingerprints.

A “production plan” can include placement of wells, length of well, depth of well, completion details, enhanced oil production methods, stimulation methods, fracking methods, order of completion, production rate, and the like. Production plans can also include well stacking, well spacing, completion designs (frac job types, job size, number of stages, number of clusters per stage, etc.) and strategies (e.g., at what sequence to frac different target zones, how to synchronize/coordinate with nearby wells, alternating or zipper fracking, etc.), production well pressure management, enhanced oil recovery strategies, and the like.

An “optimized” production plan is generated using well predictions and modeling to improve the simulated production from a well. Once a well plan is optimized, it may then be implemented at the well, at a well pad with multiple wells, or in an area penetrating one or more reservoirs and used to produce hydrocarbons or other reservoir fluids.

To “implement” an optimized plan means to actually drill and/or complete a well or wells according to the plan and then produce hydrocarbons from that well.

Reservoir performance during primary depletion is controlled largely by the natural drive mechanisms present. bOnce the drive mechanisms are known, material balance methods may be used to analyze and predict reservoir performance. Drive mechanisms are the natural sources of reservoir energy which cause oil and gas to flow into a wellbore. The three primary reservoir drive mechanisms are solution gas drive, gas cap drive, and water drive. Gravity drainage is a secondary drive mechanism capable of improving recovery in steeply dipping or high permeability reservoirs. The active drive mechanisms can often be identified from a reservoir's gas/oil ratio, reservoir pressure, and production rate histories. Early identification of the active drive mechanisms may be important to optimize a reservoir's performance.

As used herein, “cumulative oil” or “Cumulative Oil Produced” (COP) is the total amount of oil produced over time.

As used herein, “cumulative gas” is the total amount of gas produced over time.

As used herein, “water cut” is the ratio of water produced compared to the volume of total liquids produced.

As used herein, “production rate” is the rate of fluid production from the well. Production rates can be adjusted by changing the amount of fluid produced and are dependent upon the reservoirs rate of inflow and bottom hole pressure. Inflow performance relationship is controlled by the ratio of bottom hole pressure to production rate.

As used herein, “Gas Oil Ratio” or “GOR” is the volume of gas that is produced from crude oil when the oil is being extracted from the reservoir to the earth's surface through production tubing. This is generally related to associated gas or saturated gas in the oil reservoir. It is represented as standard cubic feet per stock tank barrel (scf/stb).

The “associated gas” is natural gas that is dissolved in the oil and is produced along with the crude oil. Heavy crude oil has low API gravity and low capacities of dissolved gas as compared to lighter crude oil.

“Steam to Oil Ratio” or “SOR” is a measure used to quantify the efficiency of production of oil from a reservoir based on steam injection into the reservoir. It can be defined as the amount of steam injected to produce one unit volume of crude oil. The steam is quantified by barrels of water used to make the steam, however. For example, a steam-oil ratio is 4.5 means that 4.5 barrels of water—converted into steam and injected into the well—were required to extract a single barrel of oil.

“API gravity” measures the relative density of petroleum liquid and water and has no dimensions. To derive the API gravity, the specific gravity (SG) is first measured using either the hydrometer, detailed in ASTM D1298 or with the oscillating U-tube method detailed in ASTM D4052. The official formula used to derive the gravity of petroleum liquids from the specific gravity (SG), as follows: API gravity=141.5/SG−131.5.

A “core” or “rock core” is a sample of rock, typically in the shape of a cylinder. Taken from the side of a drilled oil or gas well, a core is then dissected into multiple core plugs, or small cylindrical samples measuring about 1 inch in diameter and 3 inches long.

“Drilling cuttings” or “cutting samples” are the small irregular rock samples generated during drilling and returned with the drilling mud.

As used herein, the term “fracture hit” was initially coined to refer to the phenomenon of an infill-well fracture interacting with an adjacent well during the hydraulic-fracturing process. However, over time, its use has been extended to any type of well interference or interaction in unconventional reservoirs.

By “obtaining” a sample herein we do not necessarily imply contemporaneous sampling procedures because existing samples can be used where available. However, often contemporaneous sample collection will be needed, except for core or cutting samples, which may already be available.

By generating a reservoir “map” we mean that the reservoir is characterized in the three directional axes as well as the fourth time axis, but we do not necessarily imply a graphical representation thereof, as data can be maintained and accessed in many forms, including in tables. The map may be segmented into zones, where the fingerprinting data is very similar.

The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims or the specification means one or more than one, unless the context dictates otherwise.

The term “about” means the stated value plus or minus the margin of error of measurement or plus or minus 10% if no method of measurement is indicated.

The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or if the alternatives are mutually exclusive.

The terms “comprise”, “have”, “include” and “contain” (and their variants) are open-ended linking verbs and allow the addition of other elements when used in a claim.

The phrase “consisting of” is closed, and excludes all additional elements.

The phrase “consisting essentially of” excludes additional material elements, but allows the inclusions of non-material elements that do not substantially change the nature of the invention. Any claim or claim element introduced with the open transition term “comprising,” may also be narrowed to use the phrases “consisting essentially of” or “consisting of,” and vice versa. However, the entirety of claim language is not repeated verbatim in the interest of brevity herein.

The following abbreviations may be used herein:

ABBREVIATION TERM
AL           ARTIFICIAL LIFT
API          AMERICAN PETROLEUM INSTITUTE, ALSO API GRAVITY, IS A MEASURE OF
             HOW HEAVY OR LIGHT A PETROLEUM LIQUID IS COMPARED TO WATER: IF
             ITS API GRAVITY IS GREATER THAN 10, IT IS LIGHTER AND FLOATS ON
             WATER; IF LESS THAN 10, IT IS HEAVIER AND SINKS.
ASTM         AMERICAN SOCIETY FOR TESTING AND MATERIALS
BBL          BARREL
BHP          BOTTOMHOLE PRESSURE, PSIA
BVO          BULK VOLUME OIL
CPU          CENTRAL PROCESSING UNIT
CGR          CONDENSATE GAS RATIO-CGR GIVES A MEASURE OF THE LIQUID
             CONTENT TO THE VOLUME OF GAS. IT IS MEASURED IN BARRELS PER
             MILLIONS OF STANDARD CUBIC FEET (BARRELS/MMSCF).
COP          CUMULATIVE OIL PRODUCTION
CUM          CUMULATIVE
DRV          DRAINED ROCK VOLUMES
DSU          DRILLING SPACING UNIT
EOS          EQUATIONS OF STATE
ESP          ELECTRICAL SUBMERSIBLE PUMP
GHG          GREENHOUSE GAS
GOR          GAS TO OIL RATIO-WHEN OIL IS PRODUCED TO SURFACE TEMPERATURE
             AND PRESSURE IT IS USUAL FOR SOME NATURAL GAS TO COME OUT OF
             SOLUTION. THE GAS/OIL RATIO (GOR) IS THE RATIO OF THE VOLUME OF
             GAS THAT COMES OUT OF SOLUTION TO THE VOLUME OF OIL AT
             STANDARD CONDITIONS (TEMPERATURE = 273.15 K, PRESSURE = 1 BAR)
GPU          GRAPHICS PROCESSING UNIT
GUI          GRAPHICAL USER INTERFACE
LB           POUNDS
LBS          LOWER BAKKEN SHALE
MB           MIDDLE BAKKEN
MBO          THOUSAND BARREL OF CRUDE OIL
MTF          MIDDLE THREE FORKS
NDIC         NORTH DAKOTA INDUSTRIAL COMMISSION
PB           BUBBLE POINT PRESSURE, PSIA
PI           INITIAL RESERVOIR PRESSURE, PSIA
PV           PRESSURE VOLUME
PVT          PRESSURE VOLUME TEMPERATURE
PWF          FLOWING BOTTOM HOLE PRESSURE, PSIA
RAM          RANDOM ACCESS MEMORY
ROM          READ-ONLY MEMORY
RSI          INITIAL SOLUTION GOR, SCF/STB
SCF          STANDARD CUBIC FOOT
SG           GAS SATURATION, FRACTION
SGC          CRITICAL GAS SATURATION, FRACTION
SOR          STEAM TO OIL RATIO
SRV          STIMULATED ROCK VOLUMES
TLG          TIME LAPSE GEOCHEMISTRY-GEOCHEMICAL FINGERPRINTS TAKEN
             FROM A PLURALITY OF SAMPLES COLLECTED OVER TIME
UBS          UPPER BAKKEN SHALE
UR           UNCONVENTIONAL RESOURCES
USGS         US GEOLOGICAL SURVEY
UTF          UPPER THREE FORKS

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A. Map showing the study area around Nesson anticline.

FIG. 1B Main reservoir formations in the Bakken.

FIG. 2. Historical Bakken oil, gas and flaring rates from NDIC.

FIG. 3. Old completion well GOR trends as a function of oil rate and downhole measured BHP (1st year data).

FIG. 4. Old completion well longer term GOR trends as a function of oil rate and downhole measured BHP (˜5-year data).

FIG. 5. Modern completion well GOR trends as a function of oil rate and downhole measured BHP.

FIG. 6A. GOR trends by fluid property areas.

FIG. 6B. GOR rising magnitude by fluid property areas.

FIG. 7. Comparison of GOR for 2 wells on gas lift versus rod pump.

FIG. 8A. GOR rising magnitude vs. production months comparison between parent and child wells.

FIG. 8B. GOR rising magnitude vs. cumulative oil production comparison between parent and child wells.

FIG. 9A. schematic of parent vs. child well placement.

FIG. 9B. Impact of child well fracture hits on parent well GOR and oil rate uplift, tubing pressure and GOR pre- and post-fracture vs. time.

FIG. 10A. GOR rising magnitude vs. oil production rate.

FIG. 10B. GOR rising magnitude vs. gas production rate.

FIG. 11A. Bi-wing hydraulic fractures used in reservoir model for GOR study.

FIG. 11B. Grid refinement near fractures and wells used in reservoir model for GOR study.

FIG. 12A. Production history matching BHP for old completion wells.

FIG. 12B. Production history matching cumulative oil for old completion wells.

FIG. 12C. Production history matching GOR for old completion wells.

FIG. 13. Reservoir pressure and gas saturation evaluation at different GOR stages of old completion wells; where BHP>P_b, where BHP drops below P_b, Early Pressure build-up, Later Pressure build-up, and later flowing.

FIG. 14A. Production history matching of BHP for modern completion wells.

FIG. 14B. Production history matching of cumulative oil for modern completion wells.

FIG. 14C. Production history matching of GOR for modern completion wells.

FIG. 15. Reservoir pressure and gas saturation evaluation at different GOR stages of modern completion wells; where P_wf>P_b, where P_wf just dropped below P_b, and where GOR enters a plateau stage.

FIG. 16A. Old completion well forecasts of BHP.

FIG. 16B. Old completion well forecasts of GOR.

FIG. 17A. Modern completion well forecasts of BHP.

FIG. 17B. Modern completion well forecasts of GOR (short term).

FIG. 17C. Modern completion well forecasts of GOR (long term).

FIG. 17D. Modern completion well forecasts with less aggressive drawdown BHP.

FIG. 17E. Modern completion well forecasts with less aggressive drawdown GOR (short term).

FIG. 17F. Modern completion well forecasts of less aggressive drawdown GOR (long term).

FIG. 18A. Impacts of cluster spacing on BHP vs. time.

FIG. 18B. Impacts of cluster spacing on GOR vs. time.

FIG. 18C. Impacts of cluster spacing on cumulative (CUM) oil vs. time.

FIG. 18D showing tight cluster spacing (solid lines in FIG. 18A-C).

FIG. 18E showing wider cluster spacing (starred lines in FIG. 18A-C).

DETAILED DESCRIPTION OF THE DISCLOSURE

Herein, we present our findings on Bakken GOR trends based on high-quality data collected, integrated modeling and the analysis of hundreds of wells in the area.

Unlike Permian and Eagle Ford, Bakken is a hybrid play of shale and carbonate. The reservoir matrix permeability is at the low single-digit micro-Darcy range. It is about one order of magnitude higher than other shale plays, however it is still much lower than conventional reservoir permeability. This has important implications on the Bakken reservoir depletion process and GOR evolution that will be discussed later.

Bakken reservoir fluids generally fall in black oil to volatile oil fluid regimes with a wide range of initial solution GOR R_si from 500 to 2500 scf/stb in the study area. The initial reservoir pressure P_i varies from 6500 to 7500 psi. Many PVT samples were collected and tested across the field to characterize fluid properties. A field wide Equations of State (EOS) model was developed for various applications, including GOR modeling and facility design.

Completion design is a key driver of Bakken well GOR behaviors. We will use two main completion design types (old completion and new completion) to illustrate the typical GOR stages and trends in the short and intermediate-terms in our proof of concept demonstrations. Most of the wells drilled before 2016 have old completion designs—open hole sliding sleeve, few stages (30 or less) and low proppant volume (5 million lbs or less). Modern completion designs normally are associated with cemented plug-and-perf completions, more stages (30+) and larger frack job sizes (8+ million lbs proppant).

GOR Trends for Old Completion Design Wells

The old completion wells were developed when reservoir pressure was close to original virgin pressure P_i, i.e., there was no parent well depletion impact. Their stimulated rock volumes (SRV) were small with fewer long fractures, which can lead to faster drawdown and lower well productivity. These created unique GOR characteristics for this type of wells. Examples are given in FIG. 3 and FIG. 4, where oil rate, GOR and downhole gauge BHP pressure data are overlayed. The main observations are:

1) GOR started flat with GOR=R_si, while well flowing bottom hole pressure (P_wf) was above bubble point pressure (P_b=˜3000 psi), as shown in FIG. 3;

2) GOR rose while P_wf dropped below P_b (arrow 1);

3) GOR trended downward (arrow 1) when P_wf reached a temporary low limit due to rod pump production constraints in this case;

4) Post the operational shut-ins (e.g. downhole isolation for offset fracking), GOR came down to R_si (arrow 3). It rose back up as production resumed and P_wf dropped further (arrow 4). The mechanism and theory will be discussed in the modeling section.

The GOR rise-and-fall cycle repeated itself throughout the short to intermediate-term of the well production (FIG. 3). However, in each subsequent cycle, the P_wf built up to a lower pressure and GOR rose to a higher level though the rising pace was slow. This GOR rise-and-fall pattern is commonly observed among Bakken old-style completion wells.

A very strong correlation between producing GOR and BHP was observed from this data. GOR is much more sensitive to P_wf changes in unconventional reservoirs than in conventional reservoirs thanks to UR low matrix permeability and limited drained rock volume (DRV).

It is also interesting to see that P_wf gradually increased while the well was on production, as indicated by arrow 2 in FIG. 4. This could be caused by rod pump curtailed production and relatively faster pressure recovery from areas surrounding fractures, given the higher matrix permeability in the Bakken as compared against other shale plays. It is also consistent with early discussions that GOR moved downward during the P_wf rising period. This observation underscores the importance of collecting long-term downhole BHP data to understand GOR and reservoir depletion.

The flat GOR period was short, only 2 months in this example. The duration of the flat GOR is dictated by the pressure difference between P_i and P_b, and the pressure drawdown, which is a function of well operating practices and stimulated hydraulic fracture system supporting the well deliverability.

GOR Trends for Modern Completion Design Wells

Modern completion wells show different GOR trends from old completion wells. An example is given in FIG. 5, where oil rates, GOR and downhole gauge BHP pressure data are overlayed. The main observations are given below:

1) The flat GOR period lasted longer (5+ months). The modern completion designs created much larger SRV/DRV and resulted in higher well productivity, which can keep p_wf above p_b for a longer time.

2) GOR started rising as P_wf dropped below P_b, but a modern completion well GOR rises slower than an old completion well in the early time as compared to later (Arrow 1 v. 2). The rising periods can last from several months to over a year in Bakken.

3) GOR reached a plateau as P_wf further decreased in the intermediate term. The plateau had not been well established in this particular case.

It is also worth mentioning that Bakken GOR data do not show strong critical gas saturation (S_gc) behavior, i.e., GOR does not show a clear dip right before it starts rising. Our interpretation is because the drainage areas are very limited prior to gas breakout, it does not take a large amount of free gas to make the drained areas reach S_gc. This may create a short GOR dipping period (if there is one), which can be easily masked by the producing GOR fluctuations.

Building on the GOR stages identified in the previous sections, we also investigated other GOR trend drivers based on hundreds of Bakken wells' production data with the focus on modern completion wells in the study area.

Fluid Property Spatial Variation

The fluid property spatial variations can have significant impacts on GOR. In this study area, the south part has lower R_si and P_b, and the oil is less volatile than that in the north side. Four similar PVT property areas are defined—Areas 1, 2, 3 and 4 from south to north. The first 25-month average well GOR of each area is shown in FIG. 6A. The GOR rising magnitude, defined as normalized GOR by initial solution GOR (GOR/R_si) is shown in FIG. 6B. As the oil becomes increasingly more volatile from south areas to north areas, the initial solution GOR R_si moves higher and the producing GOR rises to a higher plateau at a faster pace.

Well Operating Strategy/Drawdown

As mentioned earlier, one of the biggest drivers in well GOR is the drawdown of the well and thus the lower P_wf. Many of the earlier wells were lifted with rod pumps after the flowing period. Often the installation of a rod pump early in the life of the well will constrain the amount of the fluid it will produce. A fluid column will then form in the well, keeping some back pressure on the formation and higher P_wf These wells would then stay above P_b longer and with less aggressive drawdown would not see the GOR rise as quickly.

Comparatively, the industry has switched to more electrical submersible pumps (ESPs) and gas lift design early on in the well life. Both are capable of handling more fluid, and in turn drawing the bottom hole pressure down lower. This more aggressive drawdown will cause more rock volume to drop below P_b and faster. The GOR is then seen to rise more early on and reach higher peak GORs than rod pump wells with similar completion designs.

Error! Reference source not found. compares 2 well's GORs on the same pad with the same formation, completion design, and timing. Both wells flowed until around October 2018 with identical GORs. After that time, one well was put on gas lift and the other on rod pump. One can see the GOR differences post AL installation with the gas lift well GOR rising much faster and higher than the other. This proves the value of reducing drawdown rates.

Offset Parent Well Depletion

As an unconventional field is developed into a mature asset, the parent (existing wells) and child (offset new wells) situation becomes more and more common, and it can have important impacts on new development well GOR. Inevitably, child wells will start production at a lower pressure than the original P_i due to the parent well depletion. This leaves smaller room for the child well's P_wf to drop before it reaches P_b. FIG. 8A compares the GOR trends between parent wells and child wells. The flat GOR period of child well is much shorter than that of parent wells. In addition, child well GOR rises to a higher plateau. FIG. 8B plots the GOR rising magnitude against cumulative oil volume instead of production months. We can draw the similar conclusion—the child well cumulative oil production is lower before the start of rising GOR.

Fracture Hits

Fracture hits (cross-well communication created by hydraulic fracturing) on parent wells are created by offset well fracturing. A parent-child well schematic is given in FIG. 9A. Since the stress and pressure near parent well areas are lower due to depletion, child hydraulic fractures tend to asymmetrically grow towards parent wells and generate strong fracture hits, as indicated by the tubing pressure jump post fracture hits in FIG. 9B. In the Bakken, the parent well production normally benefits from fracture hits. See the oil rate uplift post fracture hits in FIG. 9B. Also, it is worth noting that the rising GOR prior to fracture hits is suppressed to a much lower level for a long period time. Possible reasons include re-pressurization of the parent well prevents more areas dropping below P_b, or fracture hits create new fractures near parent wells that contact new higher-pressure rock volumes.

Well Oil and Gas Production Rates

Bakken well oil production rates normally begin with a plateau in the early time, given the higher reservoir pressure and facility constraints. It is also common that a well's oil rate plateau end coincides with the beginning of the GOR rise. The oil rate can show faster decline with rising GOR because the high gas mobility enables gas to move preferentially to oil from the reservoir to the wellbore. See FIG. 5 and FIG. 10A-B. Statistically, Bakken data show the GOR rising happens when oil rates drop from 700 stb/day to 300 stb/day (FIG. 10A). However, the gas production rates are relatively stable in the short- and intermediate-terms, i.e., the declining oil rate and rising GOR make the gas rate relatively flat. See FIG. 10B.

GOR Modeling and Long-Term Forecast

A GOR modeling workflow was developed to capture key GOR drivers, match available data and predict short- and long-term GOR trends.

The model includes the following key components:

1) A reservoir model with Bakken geological layers and reservoir properties (FIG. 11A).

2) Hydraulic fracture representations for old and modern completion designs. As discussed, old completion jobs created fewer fractures with a larger fracture area per fracture, whereas modern completion jobs created more complex fractures. The fracture properties were based on the insights gained from relevant data collected and model calibrations.

3) Tartan gridding near the well and fractures to better characterize the local pressure gradient and gas saturation changes (FIG. 11B).

4) PVT EOS model that was tuned to fluid sample lab data.

5) Relative permeability curves based on lab tests and production history matching model calibration.

6) High quality production data, including oil/gas/water rates and downhole measured BHP data.

The model was controlled by BHP to match oil production rates and GOR. The production history matching process was conducted in a probabilistic manner to obtain multiple equal-probable models for uncertainty quantification.

The old completion well matched results are given in FIG. 12A-C, where the early flat GOR period and intermediate term rise-and-fall patterns were successfully matched using the proposed modeling approach. The reservoir gas saturation (S_g) maps at various production periods are shown in FIG. 13 to illustrate the gas saturation evolution. At the beginning (first stage—see left-most gas saturation element), there was no free gas in the reservoir while P_wf>P_b. The second stage shows the S_g map when GOR reaches the peak of the first GOR rising cycle. Small amounts of gas came out of solution and was concentrated near fracture areas due to low reservoir matrix permeability and small drained rock volumes by that time. The third stage is the S_g map at the end of the first build-up. Some free gas from the second stage was pushed back into reservoir by recovered pressure near fractures. The fourth stage is the S_g map at a later GOR peak. The high S_g regions were expanded further from fractures, as compared with those in the second stage, due to the increased cumulative production volumes, and the partially recovered BHP pressure in this period was not able to make free gas solute back into reservoir. See the final stage (gas saturation element on the far right).

The data matched results for the modern completion well are given in FIG. 14A-C. The flat-rise-plateau GOR stages during the early and intermediate production periods were well matched.

The S_g and pressure evolution maps are shown in FIG. 15. Note that modern completion wells' higher fracture density resulted in earlier inter-fracture production interference and larger reservoir drainage volumes. We believe this is the main reason that the old completion wells' rise-and-fall GOR pattern and long-term slow rising trend were not observed in the modern completion wells.

The history matched models were used to predict long-term GOR. FIG. 16A-B shows the GOR prediction of old completion wells. The GOR increases at a slow pace in the long term.

For modern completion wells, two GOR scenarios were predicted based on the pressure drawdown or well operating strategy. A more aggressive drawdown (FIG. 17A) led to quicker GOR rise and a shorter GOR plateau period (FIG. 17B) before it changed to the downward trend in the long term (FIG. 17C). Whereas a less aggressive drawdown (Error! Reference source not found. D) slowed down GOR rise and created a prolonged GOR plateau (FIG. 17E-F). Again, this shows the importance of collecting long-term BHP data for GOR prediction.

Given the insights gained on Bakken UR well GOR trends and key drivers, some measures can be taken to mitigate rising GOR. Four potential methods related to completion design, development sequence and well operation strategy are discussed below.

1) Widen fracture cluster spacing: If the rest of completion design parameters are kept the same, widening fracture cluster spacing tends to create larger fracture area per fracture and delay production interferences between fractures, which will enhance well productivity and lower GOR as illustrated in FIG. 17A-F.

2) Recharge high GOR parent wells with offset child well fracture hits: As mentioned early, fracture hits can suppress parent well GOR rising and create parent well production uplift. We can take advantage of this unique situation in Bakken and strategically select the timing of fracturing the child well as parent wells enter high GOR stages.

3) Use less aggressive drawdown: An aggressive drawdown can create sharp pressure sinks near fractures or wells, given the low matrix permeability in unconventional reservoirs. It drops the pressure in these limited drainage areas below P_b faster and triggers the GOR rising events. On the other hand, a less aggressive drawdown will extend the flat GOR period and maximize the production prior to the accelerated decline caused by rising GOR.

4) Extended well shut-in: This might be a temporary solution, but long shut-ins can lower GOR when the production is resumed thanks to the pressure build-up. However, this lowered GOR tends to be short-lived, since the flush production post shut-ins can cause more aggressive drawdown and GOR will go back to the original rising trend within few months.

In summary, we have demonstrated comprehensive analysis and modeling studies on Bakken UR well GOR providing the benefit of forecasting, improved design, completion trends, and correlations between GOR and BHP. Forecasting GOR and gas rates are crucial to development plans, offtake strategy, facility design and flaring reduction. The Bakken field has seen a large increase in GOR and gas rates over the last several years driven by completion design, depletion, operational strategy, and fluid PVT properties. Old sliding sleeve completion well GOR shows cyclical rise-and-fall patterns with a gradually increasing long-term trend, whereas modern completion wells show rising GOR trend after their BHPs drop below P_b and then reach an intermediate-term plateau.

Strong correlations between GOR and BHP pressure were observed. Downhole gauge BHP pressure is critical data for GOR behavior analysis and long-term forecast. No strong critical gas saturation behavior was observed from Bakken wells. GOR can be used a diagnostic tool to monitor reservoir depletion and drainage. A focused GOR modeling approach can be crucial in the forecasting. Several measures can be taken to mitigate the high GOR, while ultimately improving well economics.

Hardware & Software

The present disclosure also relates to a computing apparatus for performing the operations described herein. This apparatus may be specially constructed for the required purposes of modeling, or it may comprise a general-purpose computer selectively activated or reconfigured by a spreadsheet program and reservoir simulation computer program stored in the computer. Such computer programs may be stored in a computer readable storage medium, preferably non-transitory, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.

In one embodiment, the computer system or apparatus may include graphical user interface (GUI) components such as a graphics display and a keyboard, which can include a pointing device (e.g., a mouse, trackball, or the like, not shown) to enable interactive operation. The GUI components may be used both to display data and processed data and to allow the user to select among options for implementing aspects of the method or for adding information about reservoir inputs or parameters to the computer programs. The computer system may store the results of the system and methods described above on disk storage, for later use and further interpretation and analysis. Additionally, the computer system may include on or more processors for running said spreadsheet and simulation programs.

Hardware for implementing the inventive methods may preferably include massively parallel and distributed Linux clusters, which utilize both CPU and GPU architectures. Alternatively, the hardware may use a LINUX OS, XML universal interface run with supercomputing facilities provided by Linux Networx, including the next-generation Clusterworx Advanced cluster management system.

Another system is the Microsoft Windows 7 Enterprise or Ultimate Edition (64-bit, SP1) with Dual quad-core or hex-core processor, 64 GB RAM with Fast rotational speed hard disk (10,000-15,000 rpm) or solid state drive (300 GB) with NVIDIA Quadro K5000 graphics card and multiple high resolution monitors. Of course, such systems may be updated with time, as computer hardware continues to improve at great rates.

Slower systems could also be used because the processing is less computation intensive than for example, 3D seismic processing.

Reservoir simulation programs can be any known in the art, possibly modified for use herein, or any novel purpose-built system. Existing commercial packages include MEERA, ECLIPSE, RESERVOIR GRAIL, 6X, VOXLER, SURFER, the CMG suite, LANDMARK NEXUS, and the like. Open source packages include BOAST—Black Oil Applied Simulation Tool, MRST—the MATLAB Reservoir Simulation Toolbox and OPM—The Open Porous Media (OPM).

The following references are each incorporated by reference in its entirety for all purposes:

Carlson, C. G.; Anderson, S. B.; Sedimentary and tectonic history of North Dakota part of Williston Basin. AAPG Bulletin; 49 (11): 1833-1846. doi.org/10.1306/A663386C-16C0-11D7-8645000102C1865D.

Cipolla, C.; Litvak, M.; Prasad, R. S.; McClure, M. “Case history of drainage mapping and effective fracture length in the Bakken.” Paper presented at the SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, Tex., USA, February 2020. doi.org/10.2118/199716-MS

Carlsen, M. L.; Whitson, C. H.; Alavian, A.; Martinsen, S. Ø.; Mydland, S.; Singh, K.; Younus, B.; Yusra, I. “Fluid Sampling in Tight Unconventionals.” Paper presented at the SPE Annual Technical Conference and Exhibition, Calgary, Alberta, Canada, September 2019. doi.org/10.2118/196056-MS

Gaswirth, S. B.; Marra, K. R.; Cook, T. A.; Charpentier, R. R.; Gautier, D. L.; Higley, D. K.; Klett, T. R.; Lewan, M. D.; Lillis, P. G.; Schenk, C. J.; Tennyson, M. E.; Whidden, K. J. “Assessment of undiscovered oil resources in the Bakken and Three Forks Formations, Williston Basin Province, Montana, North Dakota, and South Dakota, 2013” USGS National Assessment of Oil and Gas Sheet 2013-2013, p. 4.

Jones, R. S. “Producing-Gas/Oil-Ratio behavior of multifractured horizontal wells in tight oil reservoirs.” SPE Res Eval & Eng 20 (2017): 589-601. doi.org/10.2118/184397-PA

Lei, G.; Cheng, N. “Liquid-rich shale versus conventional depletion performance.” Paper presented at the SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, February 2014. doi.org/10.2118/167788-MS

Liu, Y.; Bordoloi, S.; McMahan, N.; Zhang, J.; Rajappa, B.; Long, H.; Michael, E. “Bakken infill pilot analysis and modeling: Characterizing unconventional reservoir potentials.” Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Virtual, July 2020. doi.org/10.15530/urtec-2020-2177

Luo, S.; Lutkenhaus, J.; Nasrabadi, H. “A framework for incorporating Nanopores in compositional simulation to model the unusually high GOR observed in shale reservoirs.” Paper presented at the SPE Reservoir Simulation Conference, Galveston, Tex., USA, April 2019. doi.org/10.2118/193884-MS

Pradhan, Y. “Observed gas-oil ratio trends in liquids rich shale reservoirs.” Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Virtual, July 2020. doi.org/10.15530/urtec-2020-3229

Raterman, K.; Liu, Y.; Warren, L. “Analysis of a drained rock volume: An Eagle Ford example.” Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Denver, Colo., USA, July 2019. doi.org/10.15530/urtec-2019-263

Raterman, K.; Liu, Y.; Roy, B.; Friehauf, K.; Thompson, B.; Janssen, A. “Analysis of a multi-well Eagle Ford pilot.” Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Virtual, July 2020. doi.org/10.15530/urtec-2020-2570

Whitson, C. H.; Sunjerga, S. “PVT in liquid-rich shale reservoirs.” Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Tex., USA, October 2012. doi.org/10.2118/155499-MS

ASTM D1298 “Standard test method for density, relative density or API gravity of crude petroleum and liquid petroleum products by hydrometer method.”

ASTM D4052 “Standard test method for density, relative density and API gravity of liquids by digital density meter.”

Claims

1) A method of optimizing oil production from one or more well(s) in a reservoir, said method comprising:

a) providing a model of one or more well(s) in a reservoir;

b) inputting well data into said model, said well data selected from geological layers, reservoir properties, fracturing data, completion data, permeability data, geochemistry, and combinations thereof;

c) inputting historical production data from said one or more well(s) into said model, said historical data selected from PVT data, BHP, oil production rates, gas production rates, water production rates, and combinations thereof;

d) controlling said model to match one or more parameters selected from production rates, gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), and combinations thereof, in a probabilistic manner to obtain a plurality of historical models;

e) verifying one or more test models against said plurality of historical models to identify an optimal model with minimum error;

f) using said optimal model to predict one or more parameters selected from production rates, gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), and combinations thereof from said well into a future;

g) optimizing a production plan using said predicted parameters;

h) implementing said optimized production plan in said well, whereby oil production is optimized as compared to a similar well produced without said optimized production plan.

2) The method of claim 1, wherein said reservoir is an unconventional reservoir.

3) The method of claim 1, wherein said reservoir is a hybrid shale and limestone reservoir.

4) The method of claim 1, wherein said model in a) has finer gridding near said one or more well(s).

5) The method of claim 4, wherein said finer gridding is Tartan gridding.

6) The method of claim 1, wherein said optimized reservoir plan includes reduced drawdown to reduce GOR.

7) The method of claim 1, wherein said optimized reservoir plan includes widening fracture cluster spacing to reduce GOR.

8) The method of claim 1, wherein said optimized reservoir plan includes recharging high GOR parent wells with offset child well fracture hits to reduce GOR.

9) The method of claim 1, wherein said optimized reservoir plan includes extending well shut-in time to reduce GOR.

10) A method of reducing GOR in oil production from one or more well (s) in a reservoir, said method comprising:

a) providing a model of one or more well(s);

b) inputting well data into said model, said well data selected from geological layers, reservoir properties, fracturing data, completion data, permeability data, geochemistry, and combinations thereof;

c) inputting historical production data from said one or more well(s) into said model, said historical data selected from PVT data, BHP, oil production rates, gas production rates, water production rates, and combinations thereof;

d) controlling said model to match one or more parameters selected from production rates, gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), or combinations thereof in a probabilistic manner to obtain a plurality of historical models;

e) verifying one or more test models against said historical models to identify an optimal model with minimum error;

f) using said optimal model to predict gas to oil ratio (GOR) from said one or more well(s) into a future;

g) optimizing a production plan using said predicted GOR to reduce gas production;

h) implementing said optimized production plan in said reservoir, whereby GOR is reduced as compared to said predicted GOR.

11) The method of claim 10, wherein said reservoir is an unconventional reservoir.

12) The method of claim 10, wherein said reservoir is a hybrid shale and limestone reservoir.

13) The method of claim 10, wherein said model in a) has finer gridding near said well.

14) The method of claim 13, wherein said finer gridding is Tartan gridding.

15) The method of claim 10, wherein said optimized reservoir plan includes reduced drawdown to reduce GOR.

16) The method of claim 10, wherein said optimized reservoir plan includes widening fracture cluster spacing to reduce GOR.

17) The method of claim 10, wherein said optimized reservoir plan includes recharging high GOR parent wells with offset child well fracture hits to reduce GOR.

18) The method of claim 10, wherein said optimized reservoir plan includes extending well shut-in to reduce GOR.

19) The method of claim 10, wherein said optimized reservoir plan includes one or more of:

a) reduced drawdown to reduce GOR;

b) widening fracture cluster spacing to reduce GOR;

c) recharging high GOR parent wells with offset child well fracture hits to reduce GOR;

d) extending well shut-in to reduce GOR;