SPE-139032-PP Field Development Strategies for Bakken Shale Formation

Bakken shale has been subjected to more attention during the last decade. Recently released reports discussing the high potential of the Bakken formation coupled with advancements in horizontal drilling, increased the interest of oil companies for investment in this field. Bakken fo rmation is comprised of three layers. In th is study upper and middle parts are the core of attention. Middle member which is believed to be the main reserve is most ly a limestone and the upper member is black shale. The upper member plays as a source and seal which has been subject to production in some parts as well. In this study, a Top-Down Intelligent Reservoir Modeling technique has been implemented to a part of Bakken shale format ion in Williston basin of North Dakota. This innovative technique utilizes a combination of conventional reservoir engineering methods, data mining and artificial intelligence to analyze the available data and to build a full field mo del that can be used for field development. Unlike conventional reservoir simulat ion techniques which require wide range of reservoir characteristics and geological data; Top-Down modeling utilizes the publicly availab le data (production data and well logs ) in order to generate reservoir model. The model accuracy can be enhanced as more detail data becomes available. The model can be used for proposing development strategies. The model is then used to identify remain ing reserves and sweet spots that can h elp operators identify infill locations. Furthermore, a pred ictive model was generated, history matched and economical analysis for some proposed new wells is performed. Introduction Unconventional Resources Oil and gas have been supplying a significant energy demand of societies during the last centuries. A considerable fraction of industrial and houshold energy demand has been supplied from oil and gas resources. Exploit ing energy resources has always been restricted to economy of the process and available technology. Oil and gas production has been started from shallower resources requiring fewer amount of investment and lower level of technology. As time passed, technology improvement and rapid demand of energy with increase in oil and gas price motivated the oil and gas industries to exploit deeper and more challenging resources. Conventional resources are those that are producible using the most developed technologies of present time (1). Unconventional resources are those that have not been considered economically feasible to be produced for decades. Unconventional resource plays require special development strategies and must meet growing challenges of water availability and transportation to produce. 2 Field Development Strategies for Bakken Shale Formation SPE 139032 Bakken Formation Bakken shale formation is the most significant tight oil play in the United States. The Bakken formation is an oil-bearing stratum which covers parts of Montana and North Dakota in its US share. Production from Bakken started more than 50 years ago. Bakken formation is comprised of three distinct layers (Upper, Middle and Lower members). The Middle member of the Bakken Formation is very fineto fine-grained arg illaceous, dolomitic sandstone to siltstone (2). The Middle member lies between the two black Upper and Lower shale members of Bakken. Based on the latest release of USGS in 2008, the undiscovered resources of Bakken format ion in its US share is estimated to be 3.65 b illion barrels of oil and relevant amount of associated gas. The latest estimate was 25 times higher than the previous estimate in 1995 of 151 million barrels. The cause of increase in this estimated value was the unique success of hydraulic fracturing and horizontal drilling in this field. The Bakken format ion belongs to late Devonian / early Mississippian age, covering 200,000 square miles of Williston basin in North Dakota and Montana continued up to Canada. Figure below shows the location of Bakken formation in Williston basin. Figure 1Location of the Bakken Formation in Williston Basin (3) Two different sections of the formation were selected for modeling Middle and Upper members of Bakken. One of the study areas is having all the wells completed in Upper Bakken and the other one in Middle Bakken. Locations of selected areas are shown in the following figure. Figure 2Location of sections selected for modeling Upper and Middle Bakken layers in st ate of North Dakot a (4) Two different models are generated for the sections. The Upper Bakken model has an area of 68,000 acres and the Middle Bakken model’s area is 165,000 acres. Bakken format ion may be observed in a wide range of depths over North Dakota state. Bakken format ion in the study area of this research was found at the depth range of 9,000 to 10,600 ft. The thickness of Middle Bakken member increases as we go more North-West. The thickness of Middle Bakken member in this study area varies from 20 to 70 ft. The thickest part of Upper Bakken member in its North Dakota share is located at the center o f the basin. The thickness of Location of Middle Bakken Model Location of Upper Bakken Model North Dakota [SPE 139032] Zargari, Mohaghegh 3 Upper Bakken member in the study area varies from 4 to 12 ft. Bakken fo rmation is a relatively tight sedimentary rock with low porosity and permeability. As a result of such low porosity and permeability, the recoverable reserve was estimated too low until latest report of USGS. The latest reports stated a significant change in recoverable reserve, influenced by technology advancement in the last decade. Well Completion and Stimulation in Bakken Formation The oil production from Bakken formation has significantly increased after the intensive application of horizontal drilling and hydraulic fracturing during the last decade (5). Horizontal lateral opens up greater exposure to the formation and hydraulic fracturing generates fractures which facilitate flu id flow to the wells. Hydraulic fracturing technology has been applied to latterly drilled wells (such cases present in this study are mostly completed in Middle Bakken member). Recent wells drilled in Bakken fo rmation are having as long as 20,000 ft of horizontal leg and several stages of hydraulic fracturing. Lateral length of the wells present in this study ranges from 1200 ft to 13,000 ft in Middle Bakken layer and hundreds of feet to 10,000 ft in Upper Bakken layer. Application of advanced drilling and completion technologies in Bakken formation is so expensive. Hydraulic fracturing process requires huge amount of injecting fluid which is mostly water or gasoline base. Several pump trucks inject the fracturing fluid at high rate and pressure in order to reach the yield po int of target format ion. The most recent released reports of drilling cost in Bakken format ion obtain a cost of between 3,500,000 to $5,000,000 per well (6). Therefore, economical considerations are the mos t important issues in development of the field. Top-Down Intelligent Reservoir Modeling Reservoir simulation and modeling is the science of understanding the reservoir behavior and predicting the future of the fie ld. Reservoir models are set of measured data which are correlated by certain functions . In traditional reservoir simulat ion the flu id flow correlat ions keep functional relationship between reservoir characteristics, completion data and production constrains. In this study, Top-Down Intelligent Reservoir Modeling has been utilized in order to generate a cohesive reservoir model which is coordinated such that it can be implemented in field development. In Top -Down Intelligent Reservoir Modeling, solid reservoir engineering techniques are coupled with geostatistic, data mining and artificial intelligence (7, 8). History matching is an essential part of reservoir modeling. By history matching, the reservoir model will be tuned with the production behavior of the reservoir in the past. In conventional reservoir simulat ion, history matching is the process in which the predictive result of the model is compared with actual data. A recurrent process of modify ing the input parameters takes place until the best match is achieved. The history matched model will be used for forecasting the future behavior of the reservoir. Top-Down Intelligent Reservoir Modeling technique has a different approach to history matching than conventional reservoir simulation. In conventional reservoir simulation, flu id flow equat ions govern the relation between static and dynamic aspects of the reservoir, whereas in Top-Down Intelligent Reservoir Modeling the intelligent model estimates relat ionship in data. Reservoir characteristics and results of production analysis are employed for building an intelligent model. Top-Down Modeling is comprised of several tasks which can be listed as follows:  Data Preparation  Reservoir Boundary Identification and Delineation  Volumetric Calculation  Geostatistics  Decline Curve Analysis  Field Wide Pattern Recognition  Intelligent History Matching  Infill Location Determination 4 Field Development Strategies for Bakken Shale Formation SPE 139032  Economical Analysis Data preparation is one of the most crucial tasks when performing Top-Down Intelligent Reservoir Modeling. Reservoir characteristics and production data are the most frequent type of the data utilized in reservoir simulation. Forecasting the production of existing and upcoming wells is a key to successful development strategies. Production forecasting vastly depends on the extent of our understanding about productivity and accessibility of hydrocarbon all around the field. Decline curve analysis technique is implemented to analyze the production behavior of the wells. The results of decline curve analysis will afterward be part of a data set which forms the res ervoir model. In Top-Down modeling we usually start with collecting the data and preparing the comprehensive data set. Static and predictive models will be built afterwards and predictive model is history matched. Using different design tools such as fuzzy pattern recognition or estimat ive models, we are able to make development decisions and forecast the production. The result ed prediction will be afterwards utilized in the model for further predict ions (Figure 3). Figure 3Schematic flowchart of Top-Down Modeling process Reservoir Management Petroleum reservoir management is the application of state of-the-art technology to explo it a reservoir while minimum capital investment and operation cost is used to achieve the maximum economic recovery of oil o r gas from the field (9). Reservoir management is comprised of set of operations and decisions, by which a reservoir is identified, estimat ed, developed and evaluated from its explorat ion through depletion (10). Development of a field primarily starts with drilling some exp loratory wells in the field. More wells will be drilled when productivity of the field is proved. There are many aspects which should be considered when making decisions for development of a field. Reservoir properties, geological and environmental consideration are the keys to field development. In this study, Top-Down Intelligent Reservoir Modeling has been utilized in order to generate a cohesive reservoir model which is coordinated such that it can be implemented in field development. Methodology Two concurrent approaches of Top-Down modeling were carried out in this study. One “Static Reservoir Model” and an “Intelligent History Matched-Predictive Model” were generated. In the static reservoir model, a set of volumetric and geo-models were built, then fuzzy pattern recognition was performed. This part of study is a combination of production analysis (decline curve an alysis (DCA)), production statistics, volumetric analysis, geostatistics and data clustering. Static models can provide us with general maps of the reservoir and verify distribution of reservoir characteristics. Geostatistical maps of DCA parameters and clustered cumulative production has been generated. Geostatistical results of production behavior which provides us with grid based models can be used as estimative [SPE 139032] Zargari, Mohaghegh 5 models to determine production of new wells in the field. Economical analysis was also perfor med in order to obtain estimated rate of return fo r new wells. Reservoir characteristics, completion data and production strains and reports are employed for training an intelligent model. The model correlates static information with production data. A spontaneous process of history matching and creating a predictive model is carried out. An intelligent model is built which carries completion informat ion and reservoir characteristics. The intelligent model is trained, calibrated and verified using the stat ic and dynamic informat ion of the field. The process of generating an appropriate trained intelligent model can be carried out consecutively until the least error in calibrat ion results is achieved. Static Reservoir Model Location of the existing wells is shown in the maps below. The reservoir model was delineated into 5 acre grids. Reservoir boundary was identified considering location of the wells. Using voronoi graph theory, the reservoir has been delineated into segments around the wells; each section has been assigned to be drainage zone of associated well. Figure 4Steps of reservoir boundary identification and delineation (Middle Bakken model) Production data from the wells have always been a valuable data for petroleum engineers. The production from a field is the final goal and brings lots of evidences with it to the surface. Flow rate is a response of nature to the producer. Trend of changes in production tells us about depletion of the reservoir. Declination models of production have been studied widely in the past. Arps (11) presented a set of rate-time decline curves. Arps decline equation is