scholarly journals Generalized Extreme Value Statistics, Physical Scaling and Forecasts of Oil Production in the Bakken Shale

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3641 ◽  
Author(s):  
Wardana Saputra ◽  
Wissem Kirati ◽  
Tadeusz Patzek

We aim to replace the current industry-standard empirical forecasts of oil production from hydrofractured horizontal wells in shales with a statistically and physically robust, accurate and precise method of matching historic well performance and predicting well production for up to two more decades. Our Bakken oil forecasting method extends the previous work on predicting fieldwide gas production in the Barnett shale and merges it with our new scaling of oil production in the Bakken. We first divide the existing 14,678 horizontal oil wells in the Bakken into 12 static samples in which reservoir quality and completion technologies are similar. For each sample, we use a purely data-driven non-parametric approach to arrive at an appropriate generalized extreme value (GEV) distribution of oil production from that sample’s dynamic well cohorts with at least 1 , 2 , 3 , ⋯ years on production. From these well cohorts, we stitch together the P 50 , P 10 , and P 90 statistical well prototypes for each sample. These statistical well prototypes are conditioned by well attrition, hydrofracture deterioration, pressure interference, well interference, progress in technology, and so forth. So far, there has been no physical scaling. Now we fit the parameters of our physical scaling model to the statistical well prototypes, and obtain a smooth extrapolation of oil production that is mechanistic, and not just a decline curve. At late times, we add radial inflow from the outside. By calculating the number of potential wells per square mile of each Bakken region (core and noncore), and scheduling future drilling programs, we stack up the extended well prototypes to obtain the plausible forecasts of oil production in the Bakken. We predict that Bakken will ultimately produce 5 billion barrels of oil from the existing wells, with the possible addition of 2 and 6 billion barrels from core and noncore areas, respectively.

Author(s):  
Wardana Saputra ◽  
Wissem Kirati ◽  
Tadeusz Patzek

We replace the current industry-standard empirical forecasts of oil production from hydrofractured horizontal wells in shales with a statistically and physically robust, accurate and precise approach, using the Bakken shale as an illustration. The proposed oil production forecasting method extends our previous work on predicting fieldwide gas production in the Barnett shale and merges it with our new scaling of oil production in shales. We first divide the existing 14,678 horizontal oil wells in the Bakken into 12 static samples in which depositional settings and completion technologies are similar. For each sample, we construct a purely data-driven P50 well prototype by merging the GEV distribution fits of oil production from appropriate well cohorts. We fit the parameters of our physics-based scaling curve to the statistical well prototypes, and obtain their smooth extrapolations to 30 years on production. By calculating the number of potential wells of each Bakken region, and scheduling future drilling programs, we stack up the extended well prototypes to achieve the most plausible forecast. We predict that Bakken will ultimately produce 5 billion barrels of oil from the existing wells, with the possible increments of 2 and 6 billion barrels from core and noncore areas.


2019 ◽  
Vol 33 (12) ◽  
pp. 12154-12169 ◽  
Author(s):  
Tadeusz W. Patzek ◽  
Wardana Saputra ◽  
Wissem Kirati ◽  
Michael Marder

2019 ◽  
Author(s):  
Tadeusz Patzek ◽  
Wardana Saputra ◽  
Wissem Kirati ◽  
Michael Marder

<p>We develop a method of predicting field-wide gas (or oil) production from unconventional reservoirs, using the Barnett shale as an illustration. Our method has six steps. First, divide a field of interest (here Barnett) into geographic/depositional regions, where -- upon statistical testing -- gas and/or oil production are statistically uniform. Second, in each region <i>i</i>, fit a generalized extreme value distribution to every cohort of gas/oil wells with 1,2,…,<i>n<sub>i</sub></i> years on production. Third, obtain accurate estimates of uncertainties in the distribution parameters for each regional well cohort. As a result, obtain <i>n<sub>i </sub></i>points for the stable mean (P<sub>50</sub>) well prototypes for each region <i>i</i>, and the corresponding high/low (P<sub>10</sub>/P<sub>90</sub>) bounds on well production. Fourth, by adjusting the producible gas/oil in place and pressure interference times between the adjacent hydrofractures, fit each statistical P<sub>50 </sub>well prototype with a physics-based scaling curve that also accounts for late-time external gas inflow. The physics-scaled well prototypes now extend 10-20 years into the future. Fifth, for each region, time-shift the dimensional, scaled well prototype and multiply it by the number of well completions during each year of field production. Add the production from all regions to match the past field production and predict decline of all wells up to current time. These well productivity estimates are more accurate and better quantified than anything a production decline curve analysis might yield. Sixth, by assuming different future drilling programs in each region, predict field production futures. We hope that the US Securities and Exchange Commission will adopt our robust, transparent approach as a new standard for booking gas (and oil) reserves in shale wells.</p><p></p>


2019 ◽  
Author(s):  
Tadeusz Patzek ◽  
Wardana Saputra ◽  
Wissem Kirati ◽  
Michael Marder

<p>We develop a method of predicting field-wide gas (or oil) production from unconventional reservoirs, using the Barnett shale as an illustration. Our method has six steps. First, divide a field of interest (here Barnett) into geographic/depositional regions, where -- upon statistical testing -- gas and/or oil production are statistically uniform. Second, in each region <i>i</i>, fit a generalized extreme value distribution to every cohort of gas/oil wells with 1,2,…,<i>n<sub>i</sub></i> years on production. Third, obtain accurate estimates of uncertainties in the distribution parameters for each regional well cohort. As a result, obtain <i>n<sub>i </sub></i>points for the stable mean (P<sub>50</sub>) well prototypes for each region <i>i</i>, and the corresponding high/low (P<sub>10</sub>/P<sub>90</sub>) bounds on well production. Fourth, by adjusting the producible gas/oil in place and pressure interference times between the adjacent hydrofractures, fit each statistical P<sub>50 </sub>well prototype with a physics-based scaling curve that also accounts for late-time external gas inflow. The physics-scaled well prototypes now extend 10-20 years into the future. Fifth, for each region, time-shift the dimensional, scaled well prototype and multiply it by the number of well completions during each year of field production. Add the production from all regions to match the past field production and predict decline of all wells up to current time. These well productivity estimates are more accurate and better quantified than anything a production decline curve analysis might yield. Sixth, by assuming different future drilling programs in each region, predict field production futures. We hope that the US Securities and Exchange Commission will adopt our robust, transparent approach as a new standard for booking gas (and oil) reserves in shale wells.</p><p></p>


2014 ◽  
Vol 59 (4) ◽  
pp. 987-1004 ◽  
Author(s):  
Łukasz Klimkowski ◽  
Stanisław Nagy

Abstract Multi-stage hydraulic fracturing is the method for unlocking shale gas resources and maximizing horizontal well performance. Modeling the effects of stimulation and fluid flow in a medium with extremely low permeability is significantly different from modeling conventional deposits. Due to the complexity of the subject, a significant number of parameters can affect the production performance. For a better understanding of the specifics of unconventional resources it is necessary to determine the effect of various parameters on the gas production process and identification of parameters of major importance. As a result, it may help in designing more effective way to provide gas resources from shale rocks. Within the framework of this study a sensitivity analysis of the numerical model of shale gas reservoir, built based on the latest solutions used in industrial reservoir simulators, was performed. The impact of different reservoir and hydraulic fractures parameters on a horizontal shale gas well production performance was assessed and key factors were determined.


2021 ◽  
Vol 11 (6) ◽  
pp. 2807
Author(s):  
Nediljka Gaurina-Međimurec ◽  
Vladislav Brkić ◽  
Matko Topolovec ◽  
Petar Mijić

Hydraulic fracturing operations are performed to enhance well performance and to achieve economic success from improved production rates and the ultimate reserve recovery. To achieve these goals, fracturing fluid is pumped into the well at rates and pressures that result in the creation of a hydraulic fracture. Fracturing fluid selection presents the main requirement for the successful performance of hydraulic fracturing. The selected fracturing fluid should create a fracture with sufficient width and length for proppant placement and should carry the proppant from the surface to the created fracture. To accomplish all those demands, additives are added in fluids to adjust their properties. This paper describes the classification of fracturing fluids, additives for the adjustment of fluid properties and the requirements for fluid selection. Furthermore, laboratory tests of fracturing fluid, fracture stimulation design steps are presented in the paper, as well as a few examples of fracturing fluids used in Croatia with case studies and finally, hydraulic fracturing performance and post-frac well production results. The total gas production was increased by 43% and condensate production by 106% in selected wells including wellhead pressure, which allowed for a longer production well life.


2021 ◽  
Author(s):  
Edet Ita Okon ◽  
Dulu Appah

Abstract To maximize production from mature fields, it is essential to identify candidate's wells that are not producing up to their potential. Performing periodic interventions or workovers in wells is an established approach for arresting production decline and maximizing production from the fields. However, for mature fields with large well counts, the process of determining the best candidates for well interventions can be complicated and tedious. This can result in less-than-optimal outcomes. Advanced data analytics modeling offers quick and easy access to important information. The main objective of this study is to identify potential candidate wells for workover operation ahead of time so that we can fix them before they become problem. This was achieved via principal component analysis with the aid of XLSTAT in Excel. In this study, we developed a model based on PCA to quickly identify and rank the workover candidate's wells. The dataset used in this project comprises of 66 oil wells and were obtained from a field operating in the Niger Delta. The first step involved data gathering and validation and uploading into XLSTAT software. Data preprocessing procedures were conducted to condition the dataset so as to give optimum performance during model development. A model was built to identify potential wells for workover operation. The results obtained here showed that the wells are separated to areas designated as (A to E). Wells found in area A indicated that they are potential candidates for workover operation. Wells found in area B showed that they are not under immediate danger, but attention would be needed to monitor and prevent increasing water and gas rates in the future. Wells found in area C indicated that they required immediate attention to prevent further decline in oil production. Likewise, wells found in Area D indicated that they also required immediate attention to prevent further decline in oil production. Finally, Wells found in Area E showed that they have highest oil production, lowest water production and moderate gas production, indicating normal condition with no immediate workover operation required. With advanced data analytics modeling, reservoir engineers or geoscientists will now see a bigger picture either field by field or reservoir by reservoir and quicky identify potential candidate wells for workover operation ahead of time before they become a problem. Hence, the results of the analysis can help us to better target wells that are potential candidates for high water cut, high WOR, High gas rates and low oil rates.


PETRO ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Abhikama Pradipta ◽  
Lestari Lestari ◽  
Samsol Samsol

<p><em>BN-52, BN-104, and BN-110 wells are located on the X field, PT. PERTAMINA FIELD RAMBA ASSET 1, South Sumatra. The three wells are oil-producing wells in field X. Using the Vogel equation, the IPR curve and maximum flow rate of each well are obtained, which are 152.14 BOPD, 57.2 BOPD, and 53.76 BOPD respectively. By using the exponential Decline Curve Analysis calculation method, it can be seen the rate of decline in production, as well as the time of well production to economic limit. The results of the Decline Curve Analysis show that the BN-52 well will still be in production until March 2022, and the BN-110 well can produce until March 2020. In the analysis with the Stiff &amp; Davis Method, carbonate deposits are proven, with each Stability Index value  +1.19, +1.60, and +1.35, whereas with the Skillman, Mcdonald &amp; Stiff method there was no scale sulfate, with S values of each well at 57,272 meq / l, 54,416 meq / l, and 55,147 meq / l. The scale causes oil production to decrease, consequently the IPR curve shifts to the left. The decreasing production of the three wells is due to a scale that inhibits the flow rate. Maximum flow rate was obtained by using the Standing correlation in each well of 100.06 BOPD, 54.53 BOPD, and 28.72 BOPD. The decline in oil production caused by scales must be handled appropriately.</em></p>


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